Lead Data Scientist, Causal Inference & Clinical Outcomes

Key Responsibilities:

• Lead the development of longitudinal, quasi-experimental clinical outcomes studies using healthcare claims and EHR data.
• Apply advanced causal inference techniques including propensity score matching, inverse probability weighting, and difference-in-differences.
• Quantify differences in hospitalization rates, readmissions, and post-discharge follow-up outcomes.

Required Qualifications:

• Extensive experience in data science, biostatistics, epidemiology, or health economics with focus on causal inference.
• Strong proficiency in Python, R, and SQL for analyzing large-scale healthcare datasets.
• Deep understanding of observational study design, confounding adjustment, and statistical validation.

Preferred Qualifications:

• Previous experience as a HEOR consultant or lead biostatistician evaluating chronic care management programs.
• Familiarity with APCM, CCM, and healthcare reimbursement models.
• Experience working with healthcare actuaries and executive-level reporting.