Lead Data Scientist, Causal Inference & Clinical Outcomes
New
J
Jaan HealthHealthcare technology
Remote – United StatesContractLead
Salary150 USD per hour
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Job Details
- Experience
- Extensive professional experience
- Required Skills
- PythonSQLRHIPAA
Requirements
- Have extensive professional experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research.
- Demonstrate expertise in causal inference and observational study design.
- Have experience evaluating healthcare interventions and measuring clinical outcomes.
- Be proficient in Python, R, and SQL.
- Have experience analyzing large-scale healthcare claims and EHR datasets.
- Understand propensity score matching, quasi-experimental research methods, statistical validation, and confounding adjustment.
- Be able to independently design and execute technically sophisticated analytical studies.
- Have experience developing statistical methodologies for clinical, actuarial, or external stakeholder review.
- Understand healthcare data privacy, HIPAA, and protected health information requirements.
- Have an advanced degree in Statistics, Data Science, Biostatistics, Epidemiology, Health Economics, or a closely related quantitative discipline.
- Preferred: experience with dbt, Amazon Redshift, and Looker.
Responsibilities
- Lead the design and execution of longitudinal, quasi-experimental clinical outcomes studies.
- Evaluate APCM and CCM programs using healthcare claims and Electronic Health Record (EHR) data.
- Define patient cohorts, comparison groups, outcome measures, and analytical frameworks.
- Measure 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 findings.
- Present results and develop executive-ready reports and supporting documentation for stakeholders.
- Review vendor deliverables, document methodologies, and transfer knowledge to the internal Business Intelligence team.
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