AI & Analytics Senior Manager

General Summary

The AI and Real-World Analytics Senior Manager will lead and advance the RWA team’s AI and automation capabilities, driving development and optimization of real-world data analysis processes. Responsibilities include deep-diving into Real-World Data (RWD) to generate data expertise, actionable insights, and optimal data use strategies; identifying automation/standardization opportunities; gathering requirements; and building AI agents using enterprise low/no-code solutions or partnering with Data, Technology, and Engineering (DTE) to deploy advanced AI solutions.

• Lead design, execution, and delivery of Real-World Evidence (RWE) analysis projects using AI/ML.
• Optimize analytics workflows and data intake/hosting workflows in partnership with DTE.
• Streamline project management (request handling, execution/delivery) to automate tracking, resource allocation, and milestone communication.
• Enhance/standardize analytic documentation using AI/ML to identify gaps and recommend best practices.
• Identify automation opportunities (DAP validation, medical code list searching, programming plan/code generation, QC, result cross-checking) and support internally developed AI solutions.
• Apply advanced AI/ML to generate insights (benchmarks, representativeness, gaps/overlaps) and address data capability limitations.
• Partner with DTE AI Engineering/AI Operations on best practices, standards, and observability; recommend tools/approaches.
• Design and execute AI/ML model validation (data splitting, metrics, cross-validation, robustness, fairness audits, monitoring).
• Train and provide technical support for end-user adoption.
• Provide thought leadership to shape technical strategy and culture of continuous improvement.
• Demonstrate knowledge of Generative AI ethics and governance frameworks (bias, privacy, transparency, accountability, compliance).

• Hands‑on data science/AI-ML (supervised/unsupervised/reinforcement; neural networks, random forests).
• Experience building/supporting AI applications using AWS/GCP/Azure; expertise in Azure AI Foundry.
• Proficiency in Python, SQL, SAS, and R (familiar with AWS environments).
• Knowledge of healthcare claims/EHR data; exposure to major U.S. RWD sources.
• Knowledge of statistical modeling and observational research.
• Problem‑solving/analytical skills; strong stakeholder communication.
• Experience in fast‑paced environments.

• Advanced degree in epidemiology, biostatistics, math, data science, or related field (required).
• 5+ years in observational research (life sciences industry or relevant academic/government/consulting); 3+ years in AI/ML‑related data engineering and automation.

• Annual bonus and annual equity awards (role eligible).
• Medical, dental, and vision benefits; paid time off; educational assistance (student loan repayment); commuting subsidy; matching charitable donations; 401(k).