What are the responsibilities and job description for the Real World Evidence Manager position at Meet Life Sciences?
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.