Evaluation Lead
Evaluation Lead
The Evaluation Lead is a senior analytical expert responsible for designing and conducting rigorous program evaluations. This role involves applying advanced statistical, econometric, and causal inference techniques to meticulously assess the impact, effectiveness, and performance of complex programs and models. The Evaluation Lead supports continuous organizational learning and data-driven decision-making through the delivery of timely, high-quality analytical products and insightful reports.
Responsibilities:
Lead the design and development of robust evaluation frameworks, methodologies, and performance metrics for various programs and initiatives.
Apply sophisticated causal inference models, including Difference-in-Differences (DiD), Inverse Probability of Treatment Weighting (IPTW), G-computation, and Regression Discontinuity Design (RDD).
Ensure that all model assumptions are validated, data quality is maintained, and analytical processes meet rigorous reproducibility and auditability standards.
Supervise and mentor analytical teams, coordinating effectively with client evaluation stakeholders to align on objectives and deliverables.
Create comprehensive evaluation reports, insightful dashboards, and detailed technical appendices, clearly articulating findings and methodologies.
Translate complex analytical findings and statistical results into clear, actionable insights for executive audiences, supporting strategic planning and policy refinement.
Maintain meticulous audit trails and ensure version-controlled documentation for all models, analyses, and data processes.
Collaborate with data engineers and data scientists to ensure data readiness and appropriate analytical environments for evaluation activities.
Present evaluation findings to diverse internal and external stakeholders, effectively communicating complex results.
Contribute to the continuous improvement of evaluation methodologies and analytical tools.
Experience Required:
10+ years of extensive experience in program evaluation, impact analysis, or health services research, with a strong focus on quantitative methods.
Demonstrated expertise with randomized control trials and quasi-experimental designs in real-world settings.
Strong familiarity with large-scale healthcare claims data and complex healthcare delivery models.
Proven ability to lead analytical teams and manage complex evaluation projects.
Certifications:None specifically required; a Ph.D. or advanced research credential in a quantitative field is strongly preferred.
Education: Ph.D. in Biostatistics, Economics, Epidemiology, Public Health, Health Policy, or a closely related quantitative field is strongly preferred.
Key Skills:
Causal Inference (DiD, IPTW, RDD)
Econometrics & Health Evaluation
R, Python, SAS Proficiency
Healthcare Data Analysis
Evaluation Framework Design
Statistical Reporting
Stakeholder Presentation
Audit-Ready Documentation
Team Leadership
Research Methodology
