Lead Data Scientist, Causal Inference & Clinical Outcomes
New
J
Jaan HealthHealthcare outcomes research
Remote – United StatesContractLead
Salary150 USD per hour
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Job Details
- Required Skills
- PythonSQLRHIPAA
Requirements
- Extensive professional experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research.
- Demonstrated expertise in causal inference and observational study design.
- 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 for clinical, actuarial, or external stakeholder review.
- Understanding of healthcare data privacy, HIPAA, and protected health information requirements.
- Advanced degree in Statistics, Data Science, Biostatistics, Epidemiology, Health Economics, or a closely related quantitative discipline.
- Preferred: Experience evaluating chronic care management, population health, value-based care, or Medicare-related programs.
- Preferred: Familiarity with APCM, CCM, and healthcare reimbursement models.
- Preferred: Knowledge of dbt, Amazon Redshift, and Looker.
- Preferred: Experience working with healthcare actuaries or validating analytical methodologies for actuarial review.
Responsibilities
- Lead the development and execution of longitudinal, quasi-experimental clinical outcomes studies.
- Evaluate APCM and CCM programs using healthcare claims and Electronic Health Record data.
- Establish patient cohorts, comparison groups, outcome definitions, and analytical frameworks.
- Quantify hospitalization, readmission, and post-discharge follow-up outcomes.
- Apply causal inference methods, including propensity score matching, inverse probability weighting, and difference-in-differences.
- Address confounding variables, selection bias, and limitations in observational healthcare data.
- Analyze healthcare claims, EHR, and patient utilization datasets using Python, R, and SQL.
- Conduct sensitivity analyses and validate the robustness of research findings.
- Present findings and develop reports and documentation for internal stakeholders, healthcare clients, clinical leadership, and actuarial teams.
- Review previous vendor deliverables, document methodologies, and transfer knowledge to the internal Business Intelligence team.
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