Job Description:

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.

Data Analyst Applied AI Intern, Buyer & Marketing - Business Intelligence (Spring 2027)