Lead Data Scientist – Causal Inference & Clinical Outcomes (Contract)
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 the opportunity to increase hours based on project needs
Compensation: $150 per hour
Reports To: CFO
Engagement Duration: Project-based, with potential for extension or expanded responsibilities
About Jaan Health / Phamily
Jaan Health is an innovative, AI-powered healthcare technology company focused on transforming proactive care management and improving patient outcomes.
Through its proprietary Phamily platform, Jaan Health enables healthcare providers, medical groups, health systems, and Accountable Care Organizations (ACOs) to deliver scalable, high-quality care management services.
Phamily leverages advanced technology and AI-enabled workflows to help providers manage chronic conditions, improve patient engagement, reduce avoidable healthcare utilization, and deliver measurable clinical and financial outcomes.
Learn more at phamily.com .
Position Overview
Jaan Health is seeking a highly experienced Lead Data Scientist specializing in Causal Inference and Clinical Outcomes Research for a flexible, remote consulting engagement.
This opportunity is ideal for a senior-level data scientist, biostatistician, health economist, or Healthcare Economics and Outcomes Research (HEOR) professional who has extensive experience evaluating the impact of healthcare interventions using real-world clinical data.
The consultant will lead the design and execution of a rigorous clinical outcomes study evaluating the effectiveness of Jaan Health's Advanced Primary Care Management (APCM) and Chronic Care Management (CCM) programs.
The primary objective is to establish statistically defensible evidence of program effectiveness, with particular emphasis on:
Hospitalization and Readmission Rates: Determining whether patients enrolled in care management programs experience fewer hospitalizations or readmissions.
Post-Discharge Follow-Up: Evaluating whether enrolled patients demonstrate improved follow-up rates after hospital discharge.
Clinical and Financial Outcomes: Quantifying measurable improvements in patient care, healthcare utilization, and program performance.
The ideal candidate will bring deep expertise in causal inference methodologies, observational healthcare data, and advanced statistical modeling, with the ability to independently lead a high-impact analytical initiative.
This position offers significant flexibility, beginning at approximately 10–15 hours per week, with the opportunity to ramp up hours as the project progresses .
Key Responsibilities
Clinical Outcomes Research & Study Design
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.
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.
Causal Inference & Advanced Statistical Modeling
Apply advanced causal inference techniques to estimate the impact of healthcare interventions.
Utilize methodologies such as propensity score matching, inverse probability weighting, difference-in-differences, and other appropriate quasi-experimental approaches.
Identify and address confounding variables, selection bias, and other limitations associated with observational healthcare data.
Develop high-dimensional statistical matching approaches for large, complex datasets.
Conduct sensitivity analyses and validate the robustness of research findings.
Healthcare Data Analysis & Technical Execution
Analyze complex healthcare claims,…