Lead Data Scientist, Causal Inference & Outcomes (Remote)

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

Job summary

Phamily is seeking a highly experienced Lead Data Scientist specializing in causal inference and clinical outcomes research for a flexible, remote consulting engagement in the United States. The senior position focuses on designing and executing rigorous real-world evidence studies to evaluate APCM and CCM programs.

Responsibilities include developing study designs, applying propensity score methods, analyzing claims and EHR data with Python, R, and SQL, and presenting findings to stakeholders.

Qualifications

• Extensive professional experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research.
• Demonstrated expertise in causal inference and observational study design.
• Proven experience evaluating healthcare interventions and measuring clinical outcomes.
• Strong proficiency in Python, R, and SQL.
• Experience analyzing large-scale healthcare claims and EHR datasets.
• Deep understanding of propensity score matching, quasi-experimental methods, statistical validation, and confounding adjustment.
• Ability to independently design and execute technically sophisticated analytical studies.
• Experience developing statistical methodologies suitable for rigorous clinical, actuarial, or external stakeholder review.
• Strong understanding of healthcare data privacy, HIPAA, and PHI requirements.
• Excellent communication skills to present complex findings to nontechnical stakeholders.

Responsibilities

• Lead the development and execution of longitudinal, quasi-experimental clinical outcomes studies.
• Evaluate the effectiveness of APCM and CCM programs using healthcare claims and EHR data.
• Establish appropriate patient cohorts, comparison groups, outcome definitions, and analytical frameworks.
• Quantify differences in hospitalization rates, readmissions, and post-discharge follow-up outcomes.
• Develop statistically rigorous, reproducible methodologies capable of withstanding actuarial and clinical review.
• Apply advanced causal inference techniques to estimate the impact of healthcare interventions.
• Utilize methodologies such as propensity score matching, IPW, DID, and other quasi-experimental approaches.
• Identify and address confounding variables, selection bias, and other limitations.
• Develop high-dimensional statistical matching approaches for large datasets.
• Conduct sensitivity analyses and validate the robustness of findings.
• Analyze complex healthcare claims, EHR, and utilization datasets.
• Develop and maintain analytical workflows using Python, R, and SQL.
• Work with dbt, Redshift, and Looker; ensure reproducibility and PHI compliance.
• Produce well-documented analytical code and reporting assets for stakeholders.
• Collaborate with BI and healthcare clients to align methodologies and outcomes.

Skills

Data science

Causal inference

Biostatistics

Epidemiology

HEOR

Python

R

SQL

Education

Advanced degree in Statistics / Data Science / Biostatistics / Epidemiology / Health Economics

Tools

dbt

Amazon Redshift

Looker

Job description

Phamily is seeking a highly experienced Lead Data Scientist specializing in causal inference and clinical outcomes research for a flexible, remote consulting engagement in the United States. The senior position focuses on designing and executing rigorous real-world evidence studies to evaluate APCM and CCM programs.

Responsibilities include developing study designs, applying propensity score methods, analyzing claims and EHR data with Python, R, and SQL, and presenting findings to stakeholders.