• Translate business questions into clear analytical approaches; perform data extraction, cleaning, EDA, and synthesis of actionable insights. • Conduct core analyses such as trend/cohort segmentation, benchmarking, anomaly detection, and data quality validation. • Deliver structured outputs (key findings, supporting evidence, recommendations, and risks) and present in reviews/stakeholder discussions. • Support end-to-end work on AI, data products, and digital platforms: requirements shaping, solution research, rapid prototyping/POCs, AI-evals, and impact measurements. • Help embed LLM capabilities into workflows - e.g., automated meeting notes and action items, text-to-structure extraction, knowledge retrieval (RAG) - assisted analysis, and report generation. • Contribute to scalable evaluation frameworks (metrics, test sets, acceptance criteria) to enable reuse and broader adoption. • Partner with Data Engineering, Statistics, Medical, and Clinical teams to track data readiness, ensure definition alignment, and maintain delivery timelines. • Maintain project artifacts (task lists, decisions, dependencies) to improve traceability, clarity, and execution velocity.