Real World Evidence Manager

Job Summary

The Manager of RWE Analytics supports the design, execution, and communication of observational studies using diverse real-world data (RWD) sources. This role provides technical leadership in epidemiologic study design, advanced analytics, and AI-driven methods to generate evidence that informs clinical development, market access, regulatory, and health policy decisions.

Responsibilities

• Design and execute real-world evidence and epidemiology studies using claims, EHR, registry, and linked datasets.
• Partner with asset and cross-functional leads to develop RWE strategies demonstrating unmet need, product differentiation, and value.
• Evaluate and assess feasibility of emerging data modalities (e.g., genomics, biomarkers, social determinants of health, clinical notes) aligned to study objectives.
• Lead hands-on analytics, including development of analysis plans, analytic datasets, table shells, programming, and statistical modeling.
• Apply advanced epidemiologic and statistical methods (e.g., survival analysis, causal inference, IPTW, MAIC).
• Explore, pilot, and scale AI/ML applications in RWD and epidemiology analytics.
• Manage external vendors and ensure quality control, code validation, and adherence to scientific and regulatory standards.
• Draft analysis reports and support publications and presentations at scientific conferences.
• Collaborate with clinical development, commercial, market access, safety, legal, and medical affairs teams.

Qualifications

• PhD or Master’s degree in Epidemiology, Biostatistics, Public Health, or a related field.
• Minimum 3 years of experience in real-world evidence generation and epidemiology analytics.
• Strong knowledge of healthcare claims, EHRs, hospital billing data, cancer registries (e.g., SEER), and linked datasets.
• Solid understanding of epidemiologic and statistical concepts, including bias, confounding, incidence measures, regression, and survival analysis.
• Hands-on proficiency with statistical programming languages (SAS, R, Python) and experience with real-world oncology data; AI/ML experience preferred.
• Oncology experience preferred.