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United States
Remote only
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Data Scientist, Causal Inference & Clinical Outcomes Contractor based in the United States.
This is a flexible, remote consulting opportunity for a senior data science or healthcare research professional with deep expertise in causal inference and clinical outcomes evaluation. You will lead the design and execution of a rigorous study assessing the effectiveness of Advanced Primary Care Management (APCM) and Chronic Care Management (CCM) programs. Using real-world healthcare claims and Electronic Health Record (EHR) data, you will measure the impact of care management interventions on hospitalizations, readmissions, post-discharge follow-up, and broader clinical and financial outcomes. You will apply advanced statistical methods to produce robust, reproducible evidence that can withstand clinical, actuarial, and external stakeholder review. Working with Business Intelligence, clinical, actuarial, and healthcare client teams, you will translate complex analyses into actionable insights and executive-ready reports. The engagement begins at approximately 10–15 hours per week, with flexibility to increase hours as project needs evolve. This role offers the opportunity to contribute independently to meaningful healthcare research and measurable improvements in patient outcomes.
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.
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.
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.
