Lead Data Scientist, Causal Inference & Clinical Outcomes Contractor

Clinical outcomes research and study design

• Lead the development and execution of longitudinal, quasi-experimental clinical outcomes studies.

• Evaluate APCM and CCM program effectiveness using healthcare claims and EHR data.

• Define patient cohorts, comparison groups, outcome measures, and appropriate analytical frameworks.

• Quantify differences in hospitalization rates, readmissions, and post-discharge follow-up outcomes.

• Establish statistically rigorous and reproducible methodologies suitable for clinical and actuarial review.

• Causal inference and statistical modeling

  • Apply advanced causal inference methods to estimate the impact of healthcare interventions.

  • Use propensity score matching, inverse probability weighting, difference-in-differences, and other appropriate quasi-experimental techniques.

  • Identify and address confounding variables, selection bias, and limitations inherent in observational healthcare data.

  • Develop high-dimensional statistical matching approaches for large and complex datasets.

  • Conduct sensitivity analyses and validate the robustness of research findings.

  • Healthcare data analysis and technical execution

    • Analyze large-scale healthcare claims, EHR, and patient utilization datasets.

    • Build 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, reproducibility, and compliance with applicable protected health information (PHI) security requirements.

    • Produce well-documented analytical code, workflows, and reporting assets for internal stakeholders.

    • Stakeholder collaboration and reporting

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

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

      • Present statistical findings and clinical outcomes clearly to technical and nontechnical audiences.

      • Prepare executive-ready presentations, research reports, and supporting documentation.

      • Translate complex analytical results into meaningful business and clinical recommendations.

      • Vendor deliverable review and knowledge transfer

        • Review analytical deliverables produced by previous external vendors.

        • Identify useful methodologies, data assets, and findings that can support ongoing research.

        • Recommend improvements to existing analytical processes and study designs.

        • Document research methodologies and transfer relevant knowledge to the internal Business Intelligence team.

        • Comply with applicable organizational security, privacy, and compliance policies, including separation-of-duties requirements, and protect the confidentiality, integrity, and availability of information and systems accessed.

        Requirements

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

        • 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 working with large-scale healthcare claims and EHR datasets.

        • Deep understanding of propensity score matching, quasi-experimental research methods, statistical validation, and confounding adjustment.

        • Ability to independently design and execute technically sophisticated analytical studies.

        • Experience developing statistical methodologies that can withstand rigorous clinical, actuarial, or external stakeholder review.

        • Strong knowledge of healthcare data privacy, HIPAA, and protected health information requirements.

        • Excellent communication skills, including the ability to explain complex analytical findings to nontechnical stakeholders.

        • 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 collaborating with healthcare actuaries or validating methodologies for actuarial review.

          • Knowledge of dbt, Amazon Redshift, and Looker.

          • Experience supporting health technology startups or rapidly growing healthcare technology organizations.

          • Strong consulting background and ability to deliver results independently.

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

          Benefits

          • Hourly compensation: USD $150 per hour.

          • Flexible part-time engagement: Approximately 10–15 hours per week initially, with the possibility of increasing hours based on project needs.

          • Remote work: Fully remote position within the United States.

          • Contract flexibility: Engagement available as a 1099 independent contractor or W-2 contract.

          • Flexible schedule: Ability to organize working hours around project deliverables, with availability for periodic stakeholder meetings and project discussions.

          • Project extension potential: Project-based engagement with the possibility of an extension or expanded responsibilities.

          • Meaningful impact: Opportunity to contribute to healthcare research focused on improving patient outcomes and measuring the effectiveness of care management programs.