Lead Data Scientist, Causal Inference & Clinical Outcomes Contractor

Phamily; Bolingbrook (IL); Hybrid; USD 193,000 - 220,000; Part time

Job summary

Phamily is seeking a Lead Data Scientist to drive causal inference and outcomes research for healthcare clients in a flexible, project-based engagement in Bolingbrook, IL. You will design quasi-experimental studies using claims and EHR data.

You will lead analytic teams, define cohorts, apply advanced methods like propensity scores and DiD, and deliver executive-ready results with robust documentation. This is a hybrid, part-time contractor role paying $150/hour.

Qualifications

• Extensive professional experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research.

Responsibilities

• Lead longitudinal quasi-experimental clinical outcomes studies.

Skills

Causal inference

Observational study design

Healthcare data analysis

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 Lead Data Scientist to support causal inference and clinical outcomes research through a flexible, project-based consulting engagement. This role centers on designing and executing quasi-experimental longitudinal studies that assess the effectiveness of APCM and CCM using real-world healthcare claims and EHR data.

What you will do

• Lead the development and execution of longitudinal, quasi-experimental clinical outcomes studies.
• Evaluate the effectiveness of APCM and CCM programs using healthcare claims and Electronic Health Record (EHR) data.
• Define patient cohorts, comparison groups, outcome definitions, and analytical frameworks.
• Quantify differences in hospitalization rates, readmissions, and post-discharge follow-up outcomes.
• Build statistically rigorous, reproducible methodologies designed to withstand actuarial and clinical review.
• Apply advanced causal inference approaches to estimate intervention impact, including propensity score matching, inverse probability weighting, and difference-in-differences.
• Identify and address confounding, selection bias, and other limitations common to observational healthcare data.
• Use high-dimensional statistical matching methods for large, complex datasets.
• Run sensitivity analyses and perform robustness validation for research findings.
• Analyze healthcare claims, EHR, and patient utilization datasets.
• Develop and maintain analytical workflows using Python, R, and SQL.
• Work within existing data environments that may include dbt, Amazon Redshift, and Looker.
• Ensure data integrity, analytical reproducibility, and compliance with applicable PHI security requirements.
• Produce well-documented analytical code and reporting assets for internal stakeholders.
• Partner directly with the Director of Business Intelligence and other internal stakeholders.
• Collaborate with healthcare clients, clinical leadership, and actuarial teams to align research methodology and outcome measures.
• Present findings in a clear, actionable format and create executive-ready presentations, reports, and supporting documentation.
• Review and assess analytical deliverables created by prior external vendors, then identify usable methods, data assets, and findings to incorporate into ongoing research.
• Recommend improvements to existing analytical processes and study designs.
• Document methodologies and transfer knowledge to the internal Business Intelligence team.

Required 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 research methodologies, 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 protected health information requirements.
• Excellent communication skills, including presenting complex findings to nontechnical stakeholders.
• Advanced degree in Statistics, Data Science, Biostatistics, Epidemiology, Health Economics, or a closely related quantitative discipline.

Technologies

• Python, R, SQL
• dbt
• Amazon Redshift
• Looker

Location, schedule, and employment details

• Location: Bolingbrook, IL (Hybrid Remote, United States)
• Employment type: Part-Time Contract (1099 Independent Contractor or W-2)
• Schedule: 10–15 hours per week initially, with potential to increase based on project needs
• Compensation: $150 per hour
• Reports to: CFO
• Engagement duration: Project-based, with potential for extension or expanded responsibilities

Preferred qualifications

• Previous experience as a Health Economics and Outcomes Research (HEOR) Consultant, Principal Data Scientist, or Lead Biostatistician.
• Experience evaluating chronic care management, population health, value-based care, or Medicare-related programs.
• Familiarity with APCM, CCM, and healthcare reimbursement models.
• Experience working with healthcare actuaries or validating analytical methodologies for actuarial review.
• Knowledge of dbt, Amazon Redshift, and Looker.
• Experience supporting health-tech startups or rapidly growing healthcare technology companies.
• Strong consulting background, including delivering results independently.
• Experience creating executive-level clinical outcomes presentations and client-facing research reports.