Data Analyst Applied AI Intern, Buyer & Marketing - Business Intelligence (Fall 2026)

About the team This role is embedded within the Data Analyst team under the Buyer & Marketing Product Line, working alongside analysts who own business metrics and stakeholder relationships. The team leverages data and AI technologies to improve analytical productivity and support business decision-making at scale, building reusable analytics solutions, automating recurring workflows, and enabling faster, more consistent insights across multiple markets. The role sits at the intersection of data analytics, business decision support, and Applied AI — centering on helping analysts and business teams scale analytical workflows through AI-powered automation, reusable data agents, Text-to-SQL systems, and internal productivity tools. Interns will have access to modern AI development tools, including Claude with dedicated usage credits. Responsibilities Partner with product managers and business stakeholders to understand business objectives, metric definitions, analytical requirements, and decision-making processes Identify recurring analytical tasks and business workflows that can be standardised or automated through AI-powered solutions Translate business knowledge — metric definitions, table relationships, calculation logic, and filtering rules — into reusable agent skills, structured runbooks, and automated workflows Support metric monitoring and anomaly detection workflows, connecting business risk signals with agent validation logic Build and improve LLM-powered analytics workflows for use cases such as Text-to-SQL, A/B testing analysis, and multi-region analytics Collaborate with regional stakeholders to understand market-specific business logic, reporting requirements, and metric definitions Use SQL, Python, Claude Code, and related AI development tools to build internal productivity tools, skill generators, SQL validators, and evaluation utilities Design test cases and validation frameworks to evaluate agent accuracy, consistency, and failure-handling behaviour. Investigate failure modes such as schema-linking errors, missing filters, incorrect denominators, empty results, and malformed structured outputs, and improve validation mechanisms accordingly Analyse workflow execution logs and traces to improve observability, maintainability, and system reliability Document best practices, workflows, and user guides to support adoption across teams and regions Requirements Final-year or penultimate-year student currently pursuing a Bachelor's or Master's degree in Business Analytics, Data Science, Computer Science, Information Systems, Statistics, Mathematics, Engineering, or a related quantitative field. Strong SQL skills, including joins, aggregations, CTEs, window functions, and general data warehouse concepts. Working knowledge of Python for automation, scripting, or data processing. Basic understanding of A/B testing, experimentation methodologies, and statistical significance. Interest in understanding how e-commerce metrics connect to business decisions and willingness to learn domain-specific analytical workflows. Strong interest in LLM applications, AI agents, workflow automation, or developer productivity tools. Ability to understand business problems, translate them into analytical questions, and design practical solutions. Comfortable working with cross-functional stakeholders, including analysts, product managers, engineers, and business teams. Strong analytical thinking, problem-solving, communication, and documentation skills. Comfortable using AI coding tools such as Claude Code while reviewing, validating, and maintaining generated outputs.