AI & Real World 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. Key Duties And Responsibilities 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). Knowledge And Skills 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. Education And Experience 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. Benefits 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). #J-18808-Ljbffr Vertex Pharmaceuticals