AI & Real World Analytics Senior Manager

Key Duties And Responsibilities

• Lead the design, execution, and delivery of Real-World Evidence (RWE) analysis projects, incorporating AI/ML methodologies
• Optimize and complete processes such as analytics project workflows and data source intake and hosting workflows by identifying inefficiencies and implementing AI/ML-driven solutions in partnership with DTE.
• Streamline project management processes, including request handling, execution, and delivery, to automate task tracking, resource allocation, and communication at key milestones.
• Enhance and standardize process documentation, utilizing AI/ML algorithms to identify common gaps in analytic programs and recommend optimal practices for improved outcomes.
• Identify automation opportunities for tasks like database analysis plan (DAP) validation, medical code list searching, programming plan and code generation, quality control, and result cross-checking.
• Manage and support internally-developed AI solutions, including access, runbooks, and operational workflows.
• Apply advanced AI/ML techniques to generate deep insights from real-world data (RWD), including benchmarks, representativeness, gaps, and overlaps, and propose innovative solutions to address data capability limitations.
• Partner with the Digital Technology and Engineering (DTE) team to understand the company’s AI/ML environment and recommend innovative tools and approaches to improve the RWA team’s efficiency and analytical capabilities.
• Work closely with DTE AI Engineering and AI Operations to establish and implement best practices, standards, and observability.
• Design and execute comprehensive AI/ML model validation strategies, including data splitting, metric selection, cross-validation, robustness testing, fairness audits, and post-deployment monitoring.

Knowledge And Skills

• Strong knowledge and hands-on experience in data science and AI/ML methodologies, including supervised, unsupervised, and reinforcement learning, as well as machine learning algorithms such as neural networks and random forests.
• Experience designing, deploying, and supporting AI applications using AWS, GCP, and Azure engineering ecosystems, with specific expertise in Azure AI Foundry.
• Proficiency in statistical programming languages such as Python, SQL, SAS, and R, with familiarity in working within AWS computational environments.
• Knowledge of healthcare claims and electronic health record (EHR) data, with exposure in major U.S. real-world data (RWD) sources.
• Knowledge of statistical modeling and observational research.
• Strong problem-solving and analytical abilities.
• Excellent communication skills for collaborating with stakeholders and bridging technical and domain teams.
• Experience working in fast-paced environment.

Education And Experience

• Advanced degrees in epidemiology, biostatistics, math, data science, or similar disciplines required.
• Minimum 5 years of experience working in observational research within the life sciences industry or relevant academic, government, or consulting environment, with at least 3 years of experience in AI/ML related data engineering and automation.