[{"role":"Data Scientist","resources":["https://www.coursera.org/specializations/data-science-python","https://www.kdnuggets.com/learn/data-science"],"blog_post":"https://www.geeksforgeeks.org/blogs/a-day-in-the-life-of-a-data-scientist/","plain_description":"Uses statistics and Python code to find patterns in data and build models that predict things, like which customers are about to cancel or how many orders will come in next month. A typical week mixes cleaning messy data, testing ideas, and explaining the results to the people who will act on them.","description":"Data Scientist — Transforms raw data into insights through data wrangling, visualization, and statistical analysis. Trains and evaluates machine learning models using Python, PyTorch, pandas, NumPy, and scikit-learn, applying techniques like feature engineering, NLP, and clustering to solve business problems."},{"role":"Data Engineer","resources":["https://www.datacamp.com/courses/intro-to-data-engineering","https://www.coursera.org/ca/articles/what-does-a-data-engineer-do-and-how-do-i-become-one"],"blog_post":"https://pub.aimind.so/the-day-in-a-life-of-a-data-engineer-b5c10fb91d3a","plain_description":"Makes sure data gets from where it is created, like the shop's tills, the app or the website, to the place where analysts can use it, every day and without breaking. When the morning sales report is empty, the data engineer finds out why and fixes it. Most of the work is writing code, often SQL and Python.","description":"A Data Engineer designs, builds, and maintains the systems and pipelines that collect, store, and process large volumes of data. They ensure data is accessible, reliable, and well-structured so analysts, data scientists, and other stakeholders can use it effectively. Their work often involves ETL/ELT processes, database management (SQL and NoSQL), data modeling, workflow orchestration, and cloud technologies. Data engineers play a key role in transforming raw data into a usable, high-quality resource that supports analytics, reporting, and machine learning initiatives."},{"role":"Data Analyst","resources":["https://www.coursera.org/articles/what-does-a-data-analyst-do-a-career-guide","https://graduate.northeastern.edu/knowledge-hub/what-does-a-data-analyst-do/"],"blog_post":"https://careerfoundry.com/en/blog/data-analytics/whats-it-like-to-actually-be-a-data-analyst/","plain_description":"Answers business questions with data: why did sales drop in March, which products sell together, how many customers came back. They pull the numbers, often with SQL or Excel, check that they are right, and turn them into charts and short explanations that managers can act on.","description":"A Data Analyst gathers, cleans, and analyzes structured data in order to answer key business questions and solve real‑world problems. They follow an iterative process—identifying relevant data, collecting and cleaning it, performing exploratory and statistical analysis, and interpreting trends or patterns. Their insights help stakeholders make informed decisions, supported by visualizations like charts, dashboards, and reports. "},{"role":"Business Analyst","resources":["https://www.coursera.org/articles/what-does-a-business-analyst-do-and-how-to-become-one","https://en.wikipedia.org/wiki/Business_analyst"],"blog_post":"https://www.nobledesktop.com/careers/business-analyst/daily-life","plain_description":"Works out what the business really needs before anything gets built or changed. A typical task: the sales team says the order process is slow, so the analyst maps every step, finds where time is lost, and writes down exactly what the developers should change. Uses Excel and some SQL to back this up with numbers.","description":"A Business Analyst interprets data and business processes to identify inefficiencies and recommend actionable improvements. They work closely with stakeholders to define and prioritize business and technical requirements, using tools like SQL, Excel, and data visualization software. By analyzing trends, building reports, and creating models, they support strategic decision-making and help align solutions with business goals. Business Analysts play a key role in bridging the gap between business needs and technology, driving efficiency and informed decisions across the organization."},{"role":"Business Intelligence (BI) Analyst","resources":["https://www.coursera.org/articles/business-intelligence-analysts-what-they-are-and-how-to-become-one","https://www.reddit.com/r/BusinessIntelligence/comments/164vwbf/what_is_the_difference_between_bi_analyst_and/"],"blog_post":"https://post.edu/blog/a-day-in-the-life-of-a-business-intelligence-analyst/","plain_description":"Builds and looks after the dashboards and reports a company uses to track how it is doing, such as weekly revenue, sales by region or customer numbers. Business intelligence, or BI, means exactly that: regular reporting that managers rely on. When a number looks odd, the BI analyst digs in and explains what happened.","description":"A Business Intelligence (BI) Analyst collects, cleans, and analyzes data—such as revenue, sales, market trends, and customer engagement metrics—to help organizations make data-driven decisions. They interpret patterns or insights, identify opportunities for improvement, and communicate findings through visualizations, dashboards, or reports, often making actionable recommendations to stakeholders. Their goal is to turn raw data into strategic intelligence that guides business planning and performance optimization."},{"role":"Machine Learning Engineer","resources":["https://www.coursera.org/articles/what-is-machine-learning-engineer","https://brainstation.io/career-guides/what-is-a-machine-learning-engineer"],"blog_post":"https://www.kdnuggets.com/2022/10/day-life-machine-learning-engineer.html","plain_description":"Takes a prediction model that a data scientist built on a laptop and turns it into software that runs for real, for example the model that recommends products to millions of visitors on a shop's website. They write a lot of code, keep the model fast and reliable, and retrain it when its predictions get worse.","description":"Machine Learning Engineers bridge the gap between data science and software engineering—transforming prototypes into scalable solutions, deploying models into production, monitoring performance, retraining when possible, and optimizing systems for efficiency and business value. "},{"role":"MLOps Engineer","resources":["https://neptune.ai/blog/mlops-engineer","https://www.coursera.org/articles/mlops-engineer","https://www.thedataops.org/what-is-the-daily-work-for-mlops-engineer/"],"blog_post":"https://medium.com/kitchen-sink-data-science/what-an-mlops-engineer-does-565d4d0adb2b","plain_description":"Keeps machine learning models running smoothly once they are live. MLOps stands for machine learning operations: the tools and routines for releasing a new model version safely, tracking which data it learned from, and raising an alert when its predictions get worse. Expect a lot of automation and cloud setup, close to software operations work.","description":"A Machine Learning Operations (MLOps) Engineer bridges the gap between machine learning development and production operations by applying best practices from DevOps, software engineering, and data engineering to ensure ML models run reliably at scale. They deploy, manage, and optimize ML model pipelines in production—handling tasks such as CI/CD, infrastructure provisioning (often via tools like Docker, Kubernetes, Terraform), experiment tracking, versioning of code/data/models, and ongoing monitoring. "},{"role":"Analytics Engineer","resources":["https://www.getdbt.com/blog/what-is-analytics-engineering","https://www.ascend.io/blog/why-analytics-engineers-are-the-new-must-hire-for-data-teams"],"blog_post":"https://www.datajobs.nl/en/knowledge-base/career-guide/careers-in-data-and-analytics/working-as-an-analytics-engineer","plain_description":"Sits between data engineers and analysts. They take raw data and turn it into clean, well-documented tables, for example one trusted table of customers and orders, so that every analyst and dashboard uses the same numbers. Most of the work is writing SQL and adding checks that keep those numbers right.","description":"An Analytics Engineer engineers clean, reliable, and analysis-ready data sets by transforming raw data using modern data stack tools and best practices like version control, testing, and CI/CD. Positioned between data engineering and analytics, they design, deploy, and document robust data models and pipelines—often using tools such as dbt, SQL, and cloud warehouses (e.g. Snowflake, BigQuery, Redshift). Their work empowers business analysts and stakeholders with polished, tested, and reusable data layers that support self-service reporting and decision-making."},{"role":"Data Architect","resources":["https://en.wikipedia.org/wiki/Data_architect","https://www.coursera.org/articles/data-architect","https://ddat-capability-framework.service.gov.uk/role/data-architect"],"blog_post":"https://www.datajobs.nl/en/knowledge-base/career-guide/careers-in-data-and-analytics/working-as-a-data-architect","plain_description":"Designs the overall plan for how a company stores and connects its data, a bit like a building architect for data. They decide which systems hold which data, how they link up, and the rules everyone follows, for example one agreed definition of a customer across sales, finance and support. A senior role after years of hands-on data work.","description":"A Data Architect is a strategic IT professional who defines and designs an organization’s data infrastructure. They develop the vision and framework—spanning conceptual, logical, and physical models—that governs how data is collected, stored, integrated, and accessed across systems. Collaborating with stakeholders, they translate business requirements into technical specifications and enforce data standards, governance, security, and performance principles to support enterprise strategy."},{"role":"Solution Architect","resources":["https://www.coursera.org/articles/solutions-architect","https://www.ardoq.com/knowledge-hub/solution-architect"],"blog_post":"https://devot.team/blog/solution-architect","plain_description":"Designs how a whole technical solution fits together before a team builds it. If a company wants a new customer portal, the solution architect decides which systems, cloud services and data it needs, how they connect and what it will cost, then guides the engineers and explains the plan to managers who are not technical.","description":"A Solutions Architect develops the overarching technical vision for solving specific business challenges. Guided by business requirements, they design, specify, and manage comprehensive system blueprints—spanning software, hardware, networks, and cloud infrastructure—and ensure these solutions align with enterprise architecture and strategic goals. Working closely with project managers, engineering teams, analysts, and stakeholders, they translate complex technical plans into implementable roadmaps, balancing performance, security, scalability, and budget constraints. Once a solution is approved, the Solutions Architect oversees its execution—monitoring progress, identifying risks or design issues, and serving as the liaison between technical and non-technical stakeholders to maintain alignment across teams "},{"role":"Data Ethicist","resources":["https://ddat-capability-framework.service.gov.uk/role/data-ethicist","https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/data-ethics-what-it-means-and-what-it-takes","https://builtin.com/articles/hire-data-ethicists#:~:text=The%20main%20daily%20responsibilities%20of,the%20political%20and%20legal%20sphere."],"blog_post":"https://letsphi.substack.com/p/philosophy-graduates-working-in-ai","plain_description":"Helps a company use data and AI fairly and responsibly. A typical question: does our hiring model treat women and men differently, and what do we do about it? They turn ideas like fairness and privacy into concrete rules and training for technical teams, and advise managers on legal and reputation risks. Often comes from social science, law or philosophy.","description":"A Data Ethicist evaluates the societal impact of data and technology, offering expert guidance to ensure ethical design and use of data-driven systems. Grounded in social science and ethics theory, they translate abstract principles—such as fairness, accountability, privacy, and bias—into actionable frameworks, policies, and training for data science and engineering teams. They act as a steward of ethical awareness: enabling others to adopt best practices in data ethics, facilitating dialogues across organizational silos, and mediating on complex dilemmas. Through research, advisory, training, and case-study–based examples, they make ethics accessible and practical for technical and nontechnical stakeholders. Additionally, they monitor evolving regulatory and legal landscapes, advise on organizational risk around data and AI, and support the integration of ethical principles into governance, frameworks, and corporate culture—helping build trust and manage reputational and legal risk."},{"role":"Data Product Owner","resources":["https://zeenea.com/what-is-a-data-product-owner-role-skills-responsibilities/","https://www.tamr.com/blog/data-product-owner-role-responsibilities","https://www.actian.com/blog/data-intelligence/what-is-a-data-product-owner-role-skills-responsibilities/","https://www.datascience-pm.com/data-product-owner/"],"blog_post":"https://resources.scrumalliance.org/Article/whats-typical-day-product-owner","plain_description":"Treats a dataset or a data tool as a product with its own users. For example, they own the company's customer data and decide what to improve next, based on what the teams using it need. They manage the delivery team's list of work, set quality targets, and make sure people can actually find and use the data.","description":"A Data Product Owner is responsible for championing data products within an organization—from vision to execution. They bridge business stakeholders, data engineers, and analysts to translate strategic needs into tactical product requirements. By setting the roadmap, managing the backlog, and defining KPIs, they guide development to ensure these data assets are accurate, discoverable, secure, and aligned with business goals. Beyond launch, they continuously monitor product performance and drive user adoption through training and support efforts. Working within modern data environments such as Data Mesh architectures, Data Product Owners manage access protocols, enforce data contracts, and uphold compliance for each data product domain—ensuring quality, governance, and accountability at scale. In essence, they bring product thinking to data—transforming raw information into reusable, governed, and value-generating products that serve diverse internal or external consumers."},{"role":"AI Product Manager","resources":["https://www.datascience-pm.com/ai-product-manager/"],"blog_post":"https://www.youtube.com/watch?v=E_rNotqs--I","plain_description":"Decides what an AI-powered product should do and why, then leads the team that builds it. For example, they might decide that a support chatbot should first handle refund questions, define what a good answer looks like, and track whether customers end up happier. Needs business sense and a solid feel for what AI can and cannot do.","description":"An AI Product Manager leads the strategic vision, development, and lifecycle management of AI-powered products and solutions. Operating at the intersection of business, technology, and data, they identify opportunities for AI to solve real-world challenges, craft a product roadmap, define the minimal viable product (MVP), and establish feedback loops for continuous iteration. In essence, the AI Product Manager combines strategic planning, technical understanding, and business acumen—transforming AI capabilities into scalable, impactful, and user-centered solutions."},{"role":"AI Product Owner","resources":["https://www.datascience-pm.com/ai-product-owner/"],"blog_post":"https://www.boardofinnovation.com/blog/your-new-role-ai-product-owner/","plain_description":"Works inside an AI development team and decides, week by week, what the team builds next. They turn business goals into small, clear tasks, check that finished features really work, and watch for problems like biased or wrong AI answers. Close to the AI Product Manager role, but focused on the day-to-day delivery of one product.","description":"An AI Product Owner serves as the voice of the customer within data-driven development frameworks like Scrum. They own and prioritize the backlog of AI features, translating business goals into well-defined user stories and sprint tasks. Working closely with stakeholders, data scientists, developers, and engineers, they guide the execution of AI initiatives—from sprint planning through retrospectives. Their responsibilities include defining the AI product vision, standardizing acceptance criteria, and ensuring high quality and reliability through testing and validation. They actively manage risks, including ethical concerns and bias in AI outputs, and monitor product performance to inform continuous improvement. Acting as a bridge between technical teams and business stakeholders, the AI Product Owner ensures that AI solutions deliver real value and remain aligned with evolving business objectives."},{"role":"AI Program Manager","resources":["https://www.datascience-pm.com/ai-program-manager/","https://agilemania.com/how-to-become-ai-product-manager"],"blog_post":"https://www.youtube.com/watch?v=Dhc1bd2StTM","plain_description":"Coordinates several AI projects across a company so they move forward together. They track plans, deadlines, budgets and risks, sort out dependencies between teams, and make sure legal and ethical checks happen. For example, they might run a program of five AI projects in a bank and report progress to its leadership every month.","description":"An AI Program Manager serves as the essential link between AI technologies, business objectives, and project execution. This role typically operates at a higher level than project managers, overseeing multiple AI and ML initiatives across an organization. They coordinate cross-functional teams, define project architectures, monitor lifecycles, manage risks, and ensure seamless alignment between strategic AI goals and technical delivery. Key responsibilities include translating business strategy into an overarching roadmap for AI applications, managing dependencies across projects, ensuring ethical and regulatory compliance, and facilitating collaboration among data science, engineering, and product leadership. Unlike an AI Product Owner, who focuses on sprint-level backlog prioritization and agile delivery for a single AI product, the AI Program Manager maintains a holistic scope—tracking progress, maintaining performance metrics, and steering program-level outcomes. In essence, the AI Program Manager orchestrates the successful implementation of AI solutions at scale, ensuring they deliver maximum business value while minimizing risk across projects."},{"role":"Product Analyst","resources":["https://www.coursera.org/articles/product-analyst","https://careerfoundry.com/en/blog/product-management/product-analyst-guide/","https://userpilot.com/blog/product-analyst/","https://productschool.com/blog/career-development/product-analyst"],"blog_post":"https://www.coursera.org/ca/articles/product-analyst","plain_description":"Uses data to help a product team decide what to build or change in an app or website. A typical question: did the new sign-up page get more people to finish signing up? They track how people use the product, run A/B tests, where half the users see a new version, and explain the results to product managers and designers.","description":"A Product Analyst plays a pivotal role in shaping product strategy through data. They gather and analyze quantitative and qualitative insights—such as usage metrics, market trends, and customer feedback—to inform decisions about features and enhancements. By tracking KPIs, user behavior, and conversion funnels, they identify patterns and opportunities for product improvement. On a daily basis, they design and evaluate A/B tests, conduct deep-dive analyses, and monitor product performance over time. Close collaboration with cross-functional teams (product management, engineering, UX, marketing, and support) ensures their data-driven recommendations align with business objectives. Ultimately, Product Analysts bridge the gap between data and actionable strategy, helping guide products toward a better market fit and user experience."},{"role":"Data Steward","resources":["https://www.contentserv.com/blog/what-does-a-data-steward-do","https://www.datainstitute.io/en/glossar/data-steward","https://www.linkedin.com/pulse/day-life-data-steward-pandoblox/","https://www.montecarlodata.com/blog-data-stewards-vs-data-analysts/","https://atlan.com/data-stewardship-101/"],"blog_post":"https://www.solarwinds.com/blog/understanding-the-role-of-a-data-steward","plain_description":"Looks after one area of company data, for example all customer records, and makes sure it is correct, clearly defined and used according to the rules. When two systems disagree about a customer's address, the data steward finds out which one is right and fixes the process behind it. Often a business expert rather than a programmer.","description":"A Data Steward is responsible for ensuring the quality, consistency, security, and compliance of specific data assets across an organization. Acting as a bridge between business units and technical teams, they maintain metadata, document business definitions, and enforce governance policies to ensure data is fit for purpose. Key aspects of their role include designing and implementing data quality rules, overseeing data lineage and accuracy, monitoring data usage, and resolving inconsistencies. They also help classify data, define retention and privacy requirements, and translate regulatory and governance policies into practical standards."},{"role":"Chief Data Officer","resources":["https://en.wikipedia.org/wiki/Chief_data_officer","https://www.ibm.com/think/topics/chief-data-officer"],"blog_post":"https://www.edq.com/resources/data-quality-infographics/inside-the-world-of-the-chief-data-officer/","plain_description":"The top executive for data in a large company, usually reporting to the CEO or the board. They set the company's overall data strategy, lead the data teams through other managers, and are responsible for data being trustworthy, secure, legal to use and actually useful to the business. A role for very experienced leaders.","description":"A Chief Data Officer (CDO) is a senior executive responsible for harnessing the full value of an organization’s data assets. They lead the enterprise-wide data strategy, overseeing governance, quality, security, analytics, and compliance, and ensure that data becomes a trusted and strategic asset "},{"role":"Chief AI Officer","resources":["https://en.wikipedia.org/wiki/Chief_AI_officer","https://www.iese.edu/standout/role-chief-ai-officer-caio/"],"blog_post":"https://www.youtube.com/watch?v=TV_mQUjukV0","plain_description":"The top executive responsible for how a company uses AI. They decide where AI is worth investing in, set rules for using it safely and legally, and work with engineering, legal and the board to make it happen across the whole company. Think of it as the AI counterpart of a Chief Data Officer: a role for very experienced leaders.","description":"A Chief AI Officer (CAIO) is a C-level executive responsible for shaping and executing an organization’s artificial intelligence agenda. They define the strategic vision for AI, guiding investments, initiatives, and governance to align with business objectives. The CAIO ensures ethical deployment and risk control, bridging technical innovation with regulatory compliance and long-term corporate goals.They lead cross-functional collaboration, working closely with data scientists, engineers, legal, compliance, and executive leadership to foster a culture where AI drives measurable value. Unlike technical-focused CTOs or CIOs, the CAIO balances deep AI knowledge with business insight to institutionalize AI adoption, monitor performance, manage AI-related risks, and champion responsible AI usage across the enterprise. In essence, the Chief AI Officer transforms emerging AI capabilities into scalable, strategic solutions, acting as both visionary and steward—aligning technology, ethics, innovation, and outcomes."},{"role":"Data Governance Manager","resources":["https://www.amurta.com/blogs/roles-and-responsibilities-of-data-governance-manager/","https://www.franklin.edu/career-guide/database-architects/what-do-data-governance-managers-do"],"blog_post":"https://www.youtube.com/watch?v=wsn7ewAxEek","plain_description":"Sets and runs the rules for how a company manages its data: who owns which data, how quality is checked, how long data is kept and who may see it. For example, they make sure every important number has an owner and one agreed definition. They coordinate the data stewards and report to senior management on how well the rules work.","description":"A Data Governance Manager defines, implements, and oversees an organization’s data governance framework, including policies, standards, and processes to ensure data quality, security, and compliance. They catalog critical data assets, establish clear ownership and accountability, and integrate governance into daily operations. Working across business and technical domains, they develop metadata practices, enforce data lifecycle management, monitor governance metrics, and coordinate with data stewards and owners. Acting as a central point of escalation, they resolve data-related risks and issues and support organizational transformation through better data practices. They also lead training and awareness initiatives to boost data literacy, advocate for ethical and compliant use of data, and present governance reports and KPIs to senior leadership. In short, they make sure data is reliably managed, trusted, and treated as a strategic organizational asset."},{"role":"Data Visualization Specialist","resources":["https://www.coursera.org/articles/data-visualization-specialist","https://www.geeksforgeeks.org/data-visualization/data-visualization-job-description/"],"blog_post":"https://www.datajobs.nl/en/knowledge-base/career-guide/careers-in-data-and-analytics/working-as-a-data-visualization-specialist","plain_description":"Turns data into charts and dashboards that people understand at a glance. For example, they might redesign a cluttered sales report so that a manager sees within ten seconds which region is behind. They work with tools like Tableau or Power BI, care a lot about design and storytelling, and work closely with analysts and business teams.","description":"A Data Visualization Specialist transforms complex data sets into clear, engaging, and interactive visual formats—such as charts, dashboards, graphs, and infographics—that help stakeholders understand key insights and make informed decisions. They collect, clean, and pre-process data, collaborate with data analysts and business teams to identify visualization requirements, and apply best practices in design, storytelling, and tool selection to ensure accuracy and readability. Proficient with platforms like Tableau, Power BI, D3.js, or Looker, they focus on visual clarity, audience accessibility, and user experience while continuously refining dashboards for performance and impact. In essence, they bridge raw data and strategic insight, using visual storytelling to drive business understanding and action."},{"role":"Data Quality Analyst","resources":["https://atlan.com/data-quality-analyst/","https://www.reddit.com/r/datascience/comments/15itqvw/difference_between_data_analyst_and_data_quality/","https://www.montecarlodata.com/blog-the-role-of-an-ai-data-quality-analyst/","https://www.montecarlodata.com/blog-data-quality-analyst/"],"blog_post":"https://bigblue.academy/en/data-quality-analyst","plain_description":"Checks that a company's data is correct and complete, and gets it fixed when it isn't. A typical task: finance notices that customer numbers differ between two reports, and the data quality analyst traces the difference back to where it starts. They set up rules and checks that catch errors early, so reports and decisions rest on reliable numbers.","description":"A Data Quality Analyst is responsible for ensuring that organizational data is accurate, complete, consistent, and reliable. They assess data quality through profiling and audits, identify anomalies or discrepancies, and implement cleansing and enrichment processes to meet defined standards. Their day-to-day activities include monitoring data quality metrics, conducting root-cause investigations into data issues, and collaborating with technical and business teams to resolve problems and improve processes. They establish data quality rules and standards, design dashboards and reports to track trends over time, and drive continuous improvement through process enhancements. In essence, the Data Quality Analyst acts as a guardian of trusted data, ensuring that downstream analytics and decision-making are based on high-integrity datasets. Without their work, business insights are prone to errors and inefficiencies"},{"role":"Data Protection Officer","resources":["https://www.edps.europa.eu/data-protection/data-protection/reference-library/data-protection-officer-dpo_en","https://en.wikipedia.org/wiki/Data_protection_officer"],"blog_post":"https://naomikorn.com/2025/07/23/a-day-in-the-life-of-a-data-protection-officer-dpo/","plain_description":"Makes sure a company handles personal data legally, especially under GDPR, the European privacy law. They advise management, check new projects for privacy risks, train staff, and are the contact person for privacy authorities and for customers who ask what data the company holds about them. Often comes from law or compliance rather than technology.","description":"A Data Protection Officer (DPO) ensures that an organization handles personal data in compliance with applicable data protection laws—particularly GDPR in Europe—and upholds individual privacy rights. Positioned independently within the organization, a DPO advises senior leadership on data compliance, implements governance frameworks, and raises awareness across teams through training and audits. They review and advise on Data Protection Impact Assessments, maintain transparency about data processing activities, and serve as the liaison with regulatory bodies and individuals exercising their privacy rights. This role is critical for embedding a culture of accountability and trust around how sensitive data is collected, stored, and used."},{"role":"Statistician","resources":["https://en.wikipedia.org/wiki/Statistician","https://www.coursera.org/articles/statistician"],"blog_post":"https://chalkdustmagazine.com/features/a-day-in-the-life-statisticians/","plain_description":"Uses statistics to answer questions reliably, and to say how sure we can be of the answer. They design surveys and experiments, such as a clinical trial or a customer survey, analyse the results, and explain what the numbers do and do not show. They work in business, healthcare, government and research, often explaining results to people without a statistics background.","description":"A Statistician designs and applies mathematical and statistical methods to collect, organize, and interpret numerical data for real-world problem-solving. They determine the data needed to answer key questions, design experiments or surveys, and ensure accurate data collection. Using techniques such as regression, hypothesis testing, and modeling, they analyze that data to identify trends and relationships. Statisticians then transform their findings into clear, actionable insights, presenting results through reports, visualizations, and briefings tailored to both technical and non-technical audiences. Working across sectors—including business, healthcare, education, and government—they bring rigor and evidence-based reasoning to decision-making. Ultimately, they act as data detectives and advisors, using statistical analysis to illuminate paths forward and guide strategic actions."},{"role":"Data Modeler","resources":["https://www.coursera.org/articles/what-is-a-data-modeler","https://www.techrepublic.com/article/data-modeler-job-description/"],"blog_post":"https://www.datajobs.nl/en/knowledge-base/career-guide/careers-in-data-and-analytics/working-as-a-data-modeler","plain_description":"Designs how data is organised in a database: which tables exist, what goes in them and how they connect. For example, they decide how customers, orders and products relate, so that reports and applications all give consistent answers. Detailed, structured work with a lot of contact with data architects and business experts.","description":"A Data Modeler is a systems analyst who designs and structures data models—conceptual, logical, and physical—that define how data is organized, related, and stored across business systems. Collaborating closely with data architects, database administrators, and business stakeholders, the Data Modeler translates real-world requirements into reliable and efficient database schemas. They analyze data requirements, create entity-relationship and dimensional models, optimize models for performance and consistency, and document metadata. They ensure data integrity, enforce naming standards, and maintain models as business needs evolve. The goal is to provide a scalable blueprint that supports analytics, reporting, and operational systems—bridging business strategy and technical implementation. In essence, a Data Modeler ensures data is structured, accessible, and aligned with organizational objectives, empowering data-driven decision-making across teams."},{"role":"BI Developer","resources":["https://www.indeed.com/career-advice/finding-a-job/business-intelligence-developer","https://www.coursera.org/articles/business-intelligence-developer","https://onlinedegrees.sandiego.edu/business-intelligence-developer/"],"blog_post":"https://www.mssqltips.com/sqlservertip/7028/bi-developer-day-in-the-life/","plain_description":"Builds the technical side of company reporting. They bring data from different systems together, prepare it in a reporting database, and create the dashboards people use every day, for example a sales dashboard that updates each morning. More technical than a BI analyst: more SQL and data preparation, less time explaining results to managers.","description":"A Business Intelligence (BI) Developer designs, builds, and maintains data and reporting solutions that turn raw data into actionable insights. They integrate data from multiple sources, develop ETL/ELT pipelines, model data into warehouses or marts, and implement semantic layers. They create and optimize dashboards, reports, and self-service analytics, ensuring performance, data quality, security, and governance. Working closely with stakeholders, they translate business questions into metrics and visuals that track performance and support informed decision-making. Ultimately, they deliver reliable, scalable BI platforms that enable the organization to understand trends and act on them."},{"role":"Quantitative Analyst","resources":["https://en.wikipedia.org/wiki/Quantitative_analysis_(finance)","https://www.investopedia.com/articles/professionals/121615/quantitative-analyst-job-description-average-salary.asp"],"blog_post":"https://www.cqf.com/blog/day-life-quantitative-analyst","plain_description":"Uses maths, statistics and programming to make money decisions in finance, for example pricing complex financial products, testing trading strategies on past market data, or measuring a bank's risk. Usually called a quant. They write code in Python, R or C++ and work closely with traders and risk managers. Usually needs a strong maths or physics background.","description":"A Quantitative Analyst applies mathematical, statistical, and computational techniques to analyze financial data, build predictive models, and support investment and risk management decisions. They design and implement sophisticated algorithms for pricing, trading, derivatives modeling, and portfolio optimization. Working with large, complex data sets, Quants typically use tools like Python, R, MATLAB, or C++ to build models that forecast market behavior, assess value, and quantify risk. Their responsibilities include validating model performance, back-testing strategies, ensuring accuracy, and monitoring model assumptions over time. They collaborate with traders, portfolio managers, and risk specialists to translate analytical results into actionable trading signals or risk controls. Ultimately, Quants bridge finance and technology—transforming data and theory into tools that drive investment strategy, risk analysis, and financial innovation."},{"role":"Data Journalist","resources":["https://en.wikipedia.org/wiki/Data_journalism","https://www.coursera.org/articles/data-journalist","https://datajournalism.com/read/handbook/one/introduction/why-is-data-journalism-important"],"blog_post":"https://www.theguardian.com/membership/datablog/2021/sep/13/numbers-you-can-tell-stories-with-a-decade-of-guardian-data-journalism","plain_description":"Finds and tells news stories with data. For example, they might collect rent prices across a city, analyse them, and show with a map and a few charts which neighbourhoods have become unaffordable. They combine reporting and writing with data analysis, and often build interactive charts for readers.","description":"A Data Journalist merges traditional journalism with data-driven analysis to uncover and communicate compelling stories grounded in facts. They collect, scrape, or request large datasets, then clean and analyze them using statistical tools to reveal trends, anomalies, and meaningful patterns. From these insights, they craft narratives—often involving charts, maps, infographics, or interactive features—to explain complex issues in ways that resonate with readers. By blending investigative reporting, technical analysis, and storytelling, data journalists turn raw information into engaging, verifiable news that illuminates pressing social, political, or economic topics."},{"role":"Applied Scientist","resources":["https://www.coursera.org/articles/applied-scientist","https://aijobs.net/insights/data-scientist-vs-research-scientist/","https://en.wikipedia.org/wiki/Applied_science"],"blog_post":"https://eugeneyan.com/speaking/data-scientist-day-to-day/","plain_description":"Takes ideas from scientific research and turns them into something that works in a real product, for example a better way for a shopping app to understand what people type into its search box. They run experiments, build and test models, often with machine learning, and work with engineers to ship them. Common at large tech companies, often after a PhD.","description":"An Applied Scientist specializes in solving real-world problems by blending scientific research with practical engineering. They develop and validate algorithms, prototypes, and data-driven models—often in areas like machine learning, natural language processing, or computer vision—and bring them into production-ready systems. Leveraging both domain knowledge and technical skills, Applied Scientists experiment with novel methods, conduct empirical studies, and iterate on solutions to optimize performance and utility."},{"role":"AI Edge Engineer","resources":["https://www.iis.fraunhofer.de/en/magazin/series/artificial-intelligence-ai-series/edge-ai--next-generation-artificial-intelligence-.html","https://www.refontelearning.com/blog/ai-edge-engineer-salary","https://www.ibm.com/think/topics/edge-ai"],"blog_post":"https://www.youtube.com/watch?v=6LzGHXaqtJk","plain_description":"Makes AI models run directly on devices such as cameras, phones, cars or factory sensors, instead of in the cloud. For example, a camera on a production line that spots faulty parts by itself, in real time. The hard part is making models small and fast enough for limited memory and battery, so the job mixes machine learning with hardware knowledge.","description":"An Edge AI Engineer specializes in deploying and optimizing artificial intelligence models directly on local devices—such as sensors, cameras, smartphones, drones, or industrial controllers—rather than relying solely on cloud infrastructure. By combining expertise in machine learning, embedded systems, and hardware-aware optimization, they ensure AI systems run efficiently under constraints of memory, compute power, and energy. In essence, they are tasked with making complex AI models lightweight, performant, and practical enough to power intelligent behaviors at the edge—enabling real-time analytics and autonomy across IoT, automotive, consumer electronics, and industrial domains."},{"role":"IoT Data Engineer","resources":["https://datapillar.ai/data-engineering-services-and-the-internet-of-things-iot-a-synergistic-relationship/","https://iabac.org/blog/data-engineering-for-internet-of-things-iot-managing-sensor-data-at-scale","https://apix-drive.com/en/blog/useful/iot-engineer-key-skills-and-responsibilities","https://www.linkedin.com/advice/3/youre-navigating-complexities-iot-devices-how-yuvoe"],"blog_post":"https://www.youtube.com/watch?v=6LzGHXaqtJk","plain_description":"A data engineer for data from connected devices, the internet of things or IoT: sensors in factories, smart meters, connected cars. They build the systems that collect huge streams of readings, often every second, clean them and store them so others can analyse them, for example to spot a machine that is about to fail.","description":"An IoT Data Engineer designs, builds, and maintains data pipelines and architectures tailored for vast streams of data generated by Internet of Things (IoT) devices. They collect data from sensors and edge systems, clean and harmonize diverse data formats, and securely ingest and store information in scalable platforms—such as time-series databases or data lakes—to support downstream analysis and decision-making. Because IoT data is typically high in volume, velocity, and variety, these engineers optimize pipelines for real-time or near-real-time processing while ensuring reliability and system performance. They also enforce security, encryption, and compliance measures to safeguard sensitive operational data across ingest, transmission, and storage layers. In summary, IoT Data Engineers enable the transformation of raw sensor and device data into trusted, analysis-ready assets—supporting real-time insights and operational decision-making in domains such as smart cities, industrial automation, healthcare, and connected products."},{"role":"Decision Scientist","resources":["https://chds.hsph.harvard.edu/approaches/what-is-decision-science/","https://www.datacamp.com/blog/what-is-decision-science"],"blog_post":"https://medium.com/data-science/is-decision-science-quietly-becoming-the-new-data-science-5616a12fa9e8","plain_description":"Uses data, statistics and an understanding of human behaviour to help a company make one specific decision well. For example: should we open a new warehouse, and where? They model the options, the costs and the uncertainty, and recommend a choice. Less about building dashboards, more about framing the decision and weighing the trade-offs.","description":"Decision Science is a multidisciplinary field that combines data analysis, statistics, behavioral insights, and optimization to support structured decision-making under uncertainty. Rather than simply identifying patterns, it focuses on framing specific problems and recommending actionable solutions using tools such as simulation models, cost-benefit analysis and optimization techniques. Drawing on economics, operations research, behavioral psychology, and statistics, decision science addresses both technical and human factors. It helps organizations make more informed, transparent, and robust choices—especially in complex or uncertain environments."},{"role":"Relevance Engineer","resources":["https://matthewbertram.com/blog/what-is-a-relevance-engineer","https://ipullrank.com/relevance-engineering-introduction","https://livebook.manning.com/concept/elasticsearch/relevance-engineer"],"blog_post":"https://ipullrank.com/relevance-engineering-introduction","plain_description":"Makes search and recommendations show the right things. When you type 'red running shoes' into a shop's search box, the relevance engineer makes sure the results really are red running shoes, in a useful order, and appear fast. They combine search technology, machine learning and lots of experiments that measure what users actually click.","description":"A Relevance Engineer specializes in crafting high-quality, prompt, and scalable search and recommendation experiences that align with user intent. Positioned at the intersection of information retrieval, AI, UX, content strategy, and engineering, they build and optimize components like search results, autocomplete, and recommendation systems to deliver meaning—not just matches. Their work combines classic algorithms (e.g. BM25, learning‑to‑rank) and advanced methods (e.g. vector search, embeddings, LLM-enhanced pipelines) to balance relevance, latency, and system stability. They define and track relevance metrics, conduct relevance tuning experiments, and collaborate with data scientists and domain experts to refine ranking models. They also ensure the robustness of infrastructure—working with backend systems, retrieval stacks, and testing frameworks—to deliver reliable performance at scale. In essence, Relevance Engineers transform ambiguous user needs into accurate, fast, and scalable discovery experiences across search surfaces and discovery platforms."},{"role":"Data Science Manager","resources":["https://www.coursera.org/learn/build-data-science-team","https://www.coursera.org/specializations/executive-data-science"],"blog_post":"https://roadmap.sh/engineering-manager","plain_description":"Leads a team of data scientists. They hire and coach people, decide with business leaders which problems are worth the team's time, review the team's work and explain the results to management. They rarely build models themselves anymore. The classic first step from senior data scientist into managing people.","description":"A Data Science Manager leads a team of data scientists and is accountable for what the team delivers, not for writing the models personally. They hire and grow people, run one-on-ones and performance reviews, and decide which problems are worth solving by working with product and business stakeholders. They shape project scope, review methodology, unblock the team, and translate model results into business decisions. It is the classic first step from senior data scientist into people leadership: technical judgement still matters, but most of the impact now comes through others."},{"role":"Data Engineering Manager","resources":["https://roadmap.sh/engineering-manager","https://www.coursera.org/learn/leading-teams"],"blog_post":"https://en.wikipedia.org/wiki/Engineering_management","plain_description":"Leads a team of data engineers who build and run the company's data systems. They hire and develop engineers, plan what the team builds next, balance new work against keeping things stable and affordable, and step in when something important breaks. Needs enough technical depth to judge design decisions, but most of the time goes to people and priorities.","description":"A Data Engineering Manager leads a team of data engineers (often also ML or platform engineers) that builds and runs an organization's data pipelines, warehouses and lakehouse platforms. They hire and develop engineers, plan the roadmap, balance new features against reliability, cost and technical debt, and coordinate with analytics, data science and product teams that depend on the platform. They keep enough technical depth to review architecture decisions and handle incidents, while spending most of their time on people, priorities and delivery."},{"role":"Analytics Manager","resources":["https://en.wikipedia.org/wiki/Analytics","https://www.coursera.org/learn/managing-data-analysis"],"blog_post":"https://www.ibm.com/think/topics/business-intelligence","plain_description":"Leads a team of analysts and owns how one part of the business uses data. They decide which of the many requests the team takes on, set standards for metrics and reports, and coach analysts on explaining results clearly. Success means managers make better decisions because of the team's work. A natural step for senior analysts who like working with the business.","description":"An Analytics Manager leads a team of data analysts, BI analysts or analytics engineers and owns how a business area uses data to make decisions. They prioritize requests from stakeholders, set standards for metrics, dashboards and analyses, coach analysts on problem framing and communication, and make sure insights actually change decisions. They often own the KPI framework and reporting for a department. It is a natural people-leadership step for senior analysts who enjoy working with the business."},{"role":"Head of Data","resources":["https://www.ibm.com/think/topics/data-strategy","https://en.wikipedia.org/wiki/Chief_analytics_officer"],"blog_post":"https://martinfowler.com/articles/data-mesh-principles.html","plain_description":"Leads the whole data department, usually through several managers: data engineering, analytics, reporting and often data science. They set the data strategy, decide team structure, budget and tools, and represent data in the company's leadership. In smaller companies this is effectively the top data job; in larger ones it sits below a Chief Data Officer.","description":"A Head of Data (or Director / VP of Data & Analytics) leads the whole data function: data engineering, analytics, BI and often data science, usually through several managers. They set the data strategy and operating model, decide team structure and budget, choose platforms and vendors, and represent data at the leadership table. They are accountable for data being trusted, used and delivering measurable business value. The role sits between team managers and a Chief Data Officer; in smaller companies it effectively is the CDO."},{"role":"Head of AI","resources":["https://a16z.com/ai-canon/","https://www.coursera.org/learn/leading-teams"],"blog_post":"https://newsletter.pragmaticengineer.com/p/ai-engineering-in-the-real-world","plain_description":"Leads a company's AI and machine learning department, usually several teams of engineers and scientists. They decide which AI projects to build, buy or stop, set standards for how models are built and tested, and hire the team leads. They work with product, legal and executives on budget and risk. Sits between an AI Lead, who runs one team, and a Chief AI Officer.","description":"A Head of AI (or Director of AI & ML) leads an organization's AI and machine learning function, typically several teams of ML engineers, AI engineers and applied scientists. They own the AI roadmap, decide which use cases to build, buy or drop, set technical standards for model development, evaluation and deployment, and hire and grow the leaders below them. They work with product, legal and executives on investment and risk. Scope is the AI function as a whole, between an AI Lead (one team) and a Chief AI Officer (company-wide agenda)."},{"role":"Tech Lead","resources":["https://en.wikipedia.org/wiki/Technical_lead","https://staffeng.com/guides/staff-archetypes/"],"blog_post":"https://newsletter.pragmaticengineer.com/p/what-is-a-staff-engineer","plain_description":"The most experienced engineer on a data, machine learning or AI team who still writes code every day. They decide how things should be built, split the work into pieces, review other people's code and help less experienced engineers grow. Usually nobody reports to them formally. It often comes before becoming an engineering manager or a staff engineer.","description":"A Tech Lead guides the technical direction of a data, ML or AI team while staying hands-on. They own architecture and design decisions, break down work, review code and models, set engineering standards, and mentor less experienced engineers. They are the go-to person for how something should be built, and they coordinate technical work with other teams and product. Tech Leads usually have no formal reports; it is the bridge role between senior engineer and either engineering manager or staff engineer."},{"role":"Staff Data Scientist","resources":["https://staffeng.com/guides/what-do-staff-engineers-actually-do/","https://roadmap.sh/ai-data-scientist"],"blog_post":"https://lethain.com/staff-engineer/","plain_description":"A very senior data scientist who keeps growing without managing people. They take on the hardest and most important problems, such as pricing or forecasting for the whole company, set the standards other data scientists follow for experiments and models, and mentor others. Their influence comes from expertise, not from having a team report to them.","description":"A Staff Data Scientist (and later Principal) is a senior individual contributor whose impact reaches beyond one team. They tackle the most ambiguous, high-stakes problems, such as pricing, forecasting, experimentation or core models, and set methodology standards for experiments, causal inference and modelling across the organization. They mentor other data scientists, review critical analyses, and influence product and business strategy through technical depth rather than formal authority. It is the senior path for people who want to grow without managing people."},{"role":"Staff Machine Learning Engineer","resources":["https://staffeng.com/book","https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops"],"blog_post":"https://newsletter.pragmaticengineer.com/p/what-is-a-staff-engineer","plain_description":"A very senior machine learning engineer who shapes how machine learning systems are built across several teams, without managing people. They design the largest systems, such as recommendations or search, solve the hardest speed and reliability problems, write the design documents others build on, and mentor engineers. A path for people who want more impact while staying deeply technical.","description":"A Staff Machine Learning Engineer (and later Principal) is a senior individual contributor who shapes how ML systems are designed and run across several teams. They lead the architecture of large ML systems such as recommendation, search, forecasting or LLM-based products, set standards for training, serving, monitoring and reliability, and solve the hardest scaling and performance problems. They mentor engineers, write design documents that others build on, and align technical direction with product goals, all without formal reports."},{"role":"AI Architect","resources":["https://learn.microsoft.com/en-us/azure/architecture/ai-ml/","https://aws.amazon.com/architecture/machine-learning/"],"blog_post":"https://huyenchip.com/2023/04/11/llm-engineering.html","plain_description":"Designs how AI fits into a company's systems as a whole. For example, they decide whether to build or buy a chatbot, which AI model to use, how it can safely read company documents, and what it will cost to run. They turn business ideas into designs that engineering teams can build, focused specifically on AI systems.","description":"An AI Architect designs how AI and ML capabilities fit into an organization's systems end to end. They choose between models, vendors and build-or-buy options, design architectures for LLM applications, retrieval (RAG), agents and classic ML, and define patterns for security, data access, cost, latency and governance. They translate business use cases into solution designs that engineering teams can implement and review them for scalability and risk. Unlike a general Solution or Data Architect, the focus is specifically on AI systems."},{"role":"AI Engineer","resources":["https://roadmap.sh/ai-engineer","https://www.coursera.org/learn/generative-ai-with-llms"],"blog_post":"https://www.latent.space/p/ai-engineer","plain_description":"Builds products and features on top of large language models, the technology behind tools like ChatGPT, rather than training models from scratch. For example, they might build an assistant that answers employees' questions from the company's own documents. They write software, design the instructions the model gets, test how good its answers are, and keep costs under control.","description":"An AI Engineer builds products and features on top of foundation models and large language models (LLMs). Rather than training models from scratch, they integrate model APIs, design prompts and context, build retrieval (RAG) pipelines, tools and workflows, evaluate output quality, and ship reliable, cost-efficient AI features into production software. The role blends software engineering with practical ML understanding and is one of the most common moves for experienced software, data and ML engineers into generative AI."},{"role":"AI Governance Lead","resources":["https://www.ibm.com/think/topics/ai-governance","https://artificialintelligenceact.eu/"],"blog_post":"https://www.nist.gov/itl/ai-risk-management-framework","plain_description":"Sets up and runs the rules that keep a company's use of AI safe, legal and trustworthy. For example, they decide which AI uses count as high risk under the EU AI Act, which tests and human checks those need, and what happens when something goes wrong. They work with legal, security, data protection and engineering teams on AI risk in daily practice.","description":"An AI Governance Lead (or Responsible AI Lead) builds and runs the framework that keeps an organization's AI use safe, compliant and trustworthy. They classify AI use cases by risk, implement regulations such as the EU AI Act and standards like the NIST AI Risk Management Framework, define policies for model documentation, testing, human oversight and incident handling, and work with legal, security, data protection and engineering teams. Compared to a Data Ethicist or Data Protection Officer, the focus is operational AI risk management across the whole model lifecycle."},{"role":"ML Platform Engineer","resources":["https://ml-ops.org/","https://roadmap.sh/mlops"],"blog_post":"https://www.databricks.com/glossary/mlops","plain_description":"Builds the shared internal tools that many data science and machine learning teams use to train, release and monitor their models, so that each team doesn't build its own. Their users are other engineers, so they care about the tools being easy, reliable and affordable. Mostly infrastructure and cloud work: a senior path for engineers who already run machine learning or cloud systems.","description":"An ML Platform Engineer builds the internal platform that lets many data science and ML teams train, deploy and monitor models without reinventing infrastructure. They design and run shared components such as feature stores, training and GPU clusters, model registries, serving infrastructure, experiment tracking and LLM gateways, usually on Kubernetes and cloud platforms. Their customers are other engineers, so they care about developer experience, reliability and cost. It is a senior path for MLOps, data and backend engineers who enjoy infrastructure."},{"role":"Data & AI Consultant","resources":["https://en.wikipedia.org/wiki/Management_consulting","https://www.coursera.org/specializations/ai-for-business-wharton"],"blog_post":"https://hbr.org/2023/11/how-to-capitalize-on-generative-ai","plain_description":"Helps client companies decide what to do with data and AI, and then make it happen. One project might be a three-month AI strategy for a retailer, the next helping a bank build a forecasting model. Fast-paced work across industries, with lots of presenting to senior managers. Needs technical credibility plus clear thinking and communication; senior consultants also win and lead projects.","description":"A Data & AI Consultant helps client organizations decide what to do with data and AI and then make it happen. Depending on the firm, work ranges from strategy (assessing data maturity, prioritizing use cases, building business cases and roadmaps) to delivery (designing and building data platforms, models or AI solutions). They work in short, intense projects across industries, present to senior stakeholders, and need to combine technical credibility with structured problem solving and communication. Senior consultants also sell and lead engagements."},{"role":"Solutions Engineer","resources":["https://en.wikipedia.org/wiki/Sales_engineering","https://www.presalescollective.com/"],"blog_post":"https://en.wikipedia.org/wiki/Solution_selling","plain_description":"The technical expert in the sales team of a company that sells a data or AI product. They meet potential customers, understand their setup and problems, give tailored demos, build small trial versions, answer the hard technical questions, and pass customer needs back to the product team. Suits experienced engineers who enjoy talking to customers; often well paid through a sales bonus.","description":"A Solutions Engineer (also Sales Engineer or Pre-Sales Engineer) is the technical expert in the sales process of a data or AI product company. They understand a prospect's architecture and problems, run tailored demos and proofs of concept, answer deep technical questions, design how the product fits the customer's stack, and feed customer needs back to product teams. The role suits experienced data and ML engineers who enjoy talking to customers and want commercial impact, and it is often well paid through a sales bonus."},{"role":"Data Product Manager","resources":["https://www.ibm.com/think/topics/data-product","https://roadmap.sh/product-manager"],"blog_post":"https://www.datamesh-architecture.com/","plain_description":"Owns a data product, such as an internal data platform, a trusted set of company data or an analytics tool, the way a product manager owns an app. They work out who uses it and what they need, set the plan, decide priorities and measure whether people really use it. More strategic and cross-team than a Data Product Owner, who runs one team's day-to-day work.","description":"A Data Product Manager owns data products, such as data platforms, curated datasets, metrics layers, APIs or internal analytics tools, the way a product manager owns a customer-facing product. They define who the users are, set the vision and roadmap, prioritize features and quality, measure adoption and value, and work with data engineers, analysts and governance teams. Compared to a Data Product Owner, who mainly manages the backlog within a delivery team, the Data Product Manager carries the strategic, cross-team product responsibility."},{"role":"Analytics Translator","resources":["https://www.coursera.org/learn/ai-for-everyone","https://en.wikipedia.org/wiki/Business_analytics"],"blog_post":"https://hbr.org/2018/02/you-dont-have-to-be-a-data-scientist-to-fill-this-must-have-analytics-role","plain_description":"Connects business experts with data and AI teams. They find the problems where data can really help, explain them so data scientists can solve them, and make sure the solution fits how people actually work, so it gets used. For example, a former logistics manager helping a data team build a delivery forecast. Needs deep business knowledge more than coding.","description":"An Analytics Translator (or Data & AI Translator) bridges business domain experts and technical data and AI teams. They identify and prioritize high-value use cases, frame business problems so data scientists can solve them, make sure solutions fit real workflows, and drive adoption so results are actually used. They need deep domain knowledge plus enough data literacy to judge feasibility. It is a strong path for experienced professionals from operations, finance, marketing or engineering domains who want to move into data and AI without becoming coders."},{"role":"Developer Advocate","resources":["https://roadmap.sh/devrel","https://developerrelations.com/"],"blog_post":"https://www.swyx.io/learn-in-public","plain_description":"Represents a data or AI platform to the developers who use it, and brings their feedback back to the company. They write tutorials and example code, give talks and workshops, build demos, and answer questions online and at events. Also called developer relations. Suits experienced engineers and data scientists who love teaching, writing and public speaking.","description":"A Developer Advocate (Developer Relations, DevRel) represents a data or AI platform to the technical community and represents the community back to the company. They write tutorials, sample code and documentation, give talks and workshops, build demos, engage with developers online and at events, and channel feedback to product and engineering. The role suits experienced engineers and data scientists who love teaching, writing and public speaking and want to stay technical while working with people."},{"role":"Forward Deployed Engineer","resources":["https://www.palantir.com/careers/","https://roadmap.sh/ai-engineer"],"blog_post":"https://newsletter.pragmaticengineer.com/p/forward-deployed-engineers","plain_description":"An engineer who works directly with a customer, often on site, to make a data or AI platform solve that customer's real problems. They learn the customer's messy daily work, connect their data, build custom tools on top of the platform, and tell the product team what they learn. Strong engineering plus consulting skills; common at AI companies and often involves travel.","description":"A Forward Deployed Engineer (FDE) is a software or AI engineer embedded directly with customers to make a data or AI platform deliver real outcomes in their environment. They sit with client teams, understand messy real-world workflows, integrate data, build custom applications, agents and pipelines on top of the platform, and feed what they learn back into the core product. Pioneered by Palantir and now common at AI companies, the role combines strong engineering with consulting skills, ownership and comfort with ambiguity and travel."},{"role":"AI Strategist","resources":["https://www.coursera.org/learn/wharton-ai-strategy-governance","https://www.ibm.com/think/topics/enterprise-ai"],"blog_post":"https://hbr.org/2023/11/how-to-capitalize-on-generative-ai","plain_description":"Decides where a company should use AI to create value, and in what order. They study the business, list possible uses of AI, estimate the cost and benefit of each, advise on building versus buying, and lay out a plan for executives. For example, deciding that an insurer should start with AI for claims handling. Suits people who combine business strategy with a solid grasp of AI.","description":"An AI Strategist (or AI Strategy Lead) decides where and how an organization should use AI to create value. They assess AI opportunities and maturity, build and prioritize a portfolio of use cases with business cases and ROI estimates, advise on build-versus-buy and vendor choices, and define the roadmap, operating model and capabilities needed to execute. They work closely with executives, business units, technology and governance teams. It suits experienced professionals who combine business strategy with a solid understanding of what AI can and cannot do."},{"role":"AI Lead","resources":["https://newsletter.pragmaticengineer.com/p/the-ai-engineering-stack","https://staffeng.com/guides/staff-archetypes/"],"blog_post":"https://applied-llms.org/","plain_description":"The hands-on leader of one AI or machine learning team. They set the technical direction, make the key design choices, review work and still build parts themselves, while also guiding and often managing a small group of engineers. They are responsible for the team's AI projects from idea to launch. Often the first leadership role in AI, between Tech Lead and Head of AI.","description":"An AI Lead is the hands-on leader of a single AI or ML team. They set the technical direction for the team's AI products, make key design and model choices, review work, and still build parts themselves, while also guiding, mentoring and often line-managing a small group of ML or AI engineers. They own delivery of the team's AI use cases end to end and keep stakeholders aligned. Scope is one team: it is the step between Tech Lead and Head of AI, and a common first leadership role in AI."},{"role":"AI Transformation Lead","resources":["https://www.ibm.com/think/topics/ai-transformation","https://www.coursera.org/learn/change-management"],"blog_post":"https://www.ibm.com/think/topics/digital-transformation","plain_description":"Helps a whole company use AI in everyday work, not just the tech team. For example, they might run a program that brings AI writing and analysis tools to 2,000 office staff, with training, new ways of working and tracking whether it really helps. Most of the job is managing change: people, communication and risk. Suits experienced program managers, consultants and leaders.","description":"An AI Transformation Lead (or AI Enablement Manager) drives AI adoption across an organization, not just inside the tech team. They run transformation programs that bring AI tools and use cases into everyday work, set up communities of practice and training to upskill employees, redesign processes around AI, track adoption and value, and manage the change, risk and communication that comes with it. The role suits experienced program managers, consultants and leaders with strong change-management skills and a practical understanding of AI."},{"role":"AI Agent Engineer","resources":["https://roadmap.sh/ai-agents","https://www.deeplearning.ai/courses/agentic-ai/"],"blog_post":"https://www.anthropic.com/engineering/building-effective-agents","plain_description":"Builds AI agents: software where an AI model plans steps and uses other tools by itself to finish a task, for example reading a customer's email, looking up the order and drafting a refund. They design those steps, set limits and human checks, and test that the agent is reliable, safe and affordable. A specialised kind of AI engineering that needs strong software skills.","description":"An AI Agent Engineer designs and builds agentic AI systems: applications where LLMs plan steps, call tools and APIs, and act with some autonomy to complete multi-step tasks. They design agent workflows and orchestration, tool interfaces, memory and context handling, guardrails and human-in-the-loop checks, and evaluate agents for reliability, cost and safety in production. It is a specialization of AI Engineering that rewards strong software engineering, systems thinking and careful evaluation."},{"role":"AI Evaluation Engineer","resources":["https://www.deeplearning.ai/short-courses/evaluating-ai-agents/","https://www.deeplearning.ai/short-courses/red-teaming-llm-applications/"],"blog_post":"https://hamel.dev/blog/posts/evals/","plain_description":"Checks whether AI systems actually do what they should. They agree with the product team what a good answer looks like, build test sets and automated checks, sometimes use one AI model to grade another, try to break the system on purpose, and watch quality after launch. A good fit for software testers, data scientists and analysts moving into AI.","description":"An AI Evaluation Engineer (or AI Quality Engineer) makes sure AI systems actually work as intended. They define quality criteria with product teams, build evaluation datasets and automated test suites for LLM and agent outputs, design LLM-as-judge and human review processes, red-team systems for failures, bias and safety issues, and monitor quality in production. The role combines software testing, statistics and ML understanding, and is a good fit for experienced QA engineers, data scientists and analysts moving into generative AI."},{"role":"Fractional Data & AI Executive","resources":["https://en.wikipedia.org/wiki/Fractional_executive","https://en.wikipedia.org/wiki/Interim_management"],"blog_post":"https://www.ibm.com/think/topics/data-strategy","plain_description":"A very experienced data or AI leader who works part-time for several companies, or temporarily for one, as their chief data or AI officer. They set strategy, build or rebuild teams, choose tools and suppliers, and advise founders and boards, usually at startups and mid-sized companies not yet ready for a full-time executive. A path for leaders who want independence and variety.","description":"A Fractional Data & AI Executive (for example a fractional CDO or Chief AI Officer) provides senior data and AI leadership to several organizations part-time, or to one organization on an interim basis. They set data and AI strategy, build or restructure teams, select platforms and vendors, and advise founders and executive boards, typically for startups and mid-sized companies that need leadership before they can justify a full-time executive. It is a path for very experienced leaders who want independence and variety."}]