Lead Data Scientist, Causal Inference & Clinical Outcomes Contractor (Bolingbrook, IL)

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, 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.

Stakeholder Collaboration & Reporting

Collaborate directly with the Director of Business Intelligence and other internal stakeholders.

Engage with healthcare clients, clinical leadership, and actuarial teams to align research methodologies and outcome measures.

Present statistical findings and clinical outcomes in a clear, actionable format.

Develop executive-ready presentations, reports, and supporting documentation.

Translate complex analytical findings into meaningful business and clinical insights.

Vendor Deliverable Review & Knowledge Transfer

Review and assess analytical deliverables produced by previous external vendors.

Identify usable methodologies, data assets, and findings that can be incorporated 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 the ability to present complex findings to nontechnical stakeholders.

Advanced degree in Statistics, Data Science, Biostatistics, Epidemiology, Health Economics, or a closely related quantitative discipline.

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 with the ability to deliver results independently.

Experience developing executive-level clinical outcomes presentations and client-facing research reports.

Contract Details & Compensation

Hourly Rate: $150 per hour.

Engagement Type: 1099 independent contractor or W-2 contract.

Initial Commitment: Approximately 10–15 hours per week.

Growth Opportunity: Potential to increase weekly hours as project requirements evolve.

Work Arrangement: Hybrid remote.

Schedule: Flexible, with availability for periodic stakeholder meetings and project discussions.

Duration: Project-based engagement with the possibility of extension.

This opportunity is particularly well suited for an experienced consultant seeking a flexible, part-time engagement while contributing to meaningful healthcare research and measurable improvements in patient outcomes.

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