Remote Lead Data Scientist — Causal Inference for Clinical Outcomes

jobsatphamily; United States; Remote; USD 170,000 - 243,000; Part time

Job summary

jobsatphamily is seeking a part-time remote data scientist to assess whether care management programs improve patient outcomes through rigorous causal analysis of claims and EHR data. The engagement is 10–15 hours per week, with potential expansion as project needs evolve.

You will design quasi-experimental studies, evaluate outcomes like hospitalizations and readmissions, apply methods such as propensity score matching and inverse probability weighting, and document methods for knowledge

Qualifications

• Extensive experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research.
• Expertise in causal inference and observational study design for healthcare interventions.
• Strong Python, R, and SQL skills with large healthcare data.
• Ability to deliver statistically rigorous work for clinical, actuarial, or external review.
• Knowledge of healthcare privacy requirements (HIPAA).

Responsibilities

• Design and execute longitudinal, quasi-experimental studies with cohorts and outcomes.
• Evaluate hospitalizations, readmissions, and post-discharge outcomes.
• Apply causal inference methods such as propensity score matching, IPW, and differences-in-differences.
• Address confounding, run sensitivity analyses, and assess robustness of results.
• Analyze claims, EHR, and utilization data; build reproducible Python/R/SQL workflows.
• Explain findings in reports and presentations; document methods for knowledge transfer.

Skills

Causal inference

Observational study design

Statistical analysis

Education

Advanced degree in a quantitative field

Tools

Python

R

SQL

Job description

jobsatphamily is seeking a part-time remote data scientist to assess whether care management programs improve patient outcomes through rigorous causal analysis of claims and EHR data. The engagement is 10–15 hours per week, with potential expansion as project needs evolve.

You will design quasi-experimental studies, evaluate outcomes like hospitalizations and readmissions, apply methods such as propensity score matching and inverse probability weighting, and document methods for knowledge