Lead Data Scientist, Causal Inference – Clinical Outcomes Contractor

This is a fully remote position, open to applicants in United States.

📋 Description

• Oversee the creation and implementation of longitudinal, quasi-experimental studies focusing on clinical outcomes.

• Assess the effectiveness of APCM and CCM programs utilizing healthcare claims and EHR data.

• Formulate patient cohorts, comparison groups, outcome definitions, and analytical frameworks.

• Measure variations in hospitalization rates, readmissions, and outcomes following discharge.

• Construct statistically robust and reproducible methodologies that are fit for actuarial and clinical evaluations.

• Employ causal inference methods such as propensity score matching, inverse probability weighting, and difference-in-differences.

• Detect and mitigate confounding variables, selection bias, and limitations inherent in observational healthcare datasets.

• Create high-dimensional statistical matching techniques for extensive, intricate datasets.

• Perform sensitivity analyses and authenticate research outcomes.

• Analyze healthcare claims, EHR, and patient utilization data.

• Design and sustain analytical workflows utilizing Python, R, and SQL.

• Operate within data environments such as dbt, Amazon Redshift, and Looker.

• Ensure the integrity of data, reproducibility of analyses, and adherence to PHI security regulations.

• Generate documented analytical code and reporting materials.

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

• Interact with healthcare clients, clinical leadership, and actuarial teams.

• Clearly present statistical results and clinical outcomes.

• Create executive-ready presentations, reports, and accompanying documentation.

• Interpret complex findings into actionable business and clinical insights.

• Review analytical outputs from previous vendors and identify reusable methodologies, data assets, and insights.

• Suggest enhancements to analytical workflows and study frameworks.

• Document methodologies and impart knowledge to the internal Business Intelligence team.

⛳️ Requirements

• Substantial professional experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research.

• Proven expertise in causal inference and the design of observational studies.

• Demonstrated experience in evaluating healthcare interventions and assessing clinical outcomes.

• High proficiency in Python, R, and SQL.

• Background in analyzing large-scale healthcare claims and EHR datasets.

• Strong grasp of propensity score matching, quasi-experimental research methodologies, statistical validation, and confounding adjustment.

• Capability to independently design and conduct technically advanced analytical studies.

• Experience in developing statistical methodologies suitable for rigorous clinical, actuarial, or stakeholder review.

• Comprehensive understanding of healthcare data privacy, HIPAA, and requirements for protected health information.

• Exceptional communication skills, including the ability to convey complex findings to non-technical audiences.

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

• Familiarity with APCM, CCM, and healthcare reimbursement frameworks.

• Knowledge of dbt, Amazon Redshift, and Looker.

• Experience supporting health-tech startups or rapidly growing healthcare technology firms.

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

🏝️ Benefits

• Flexible, part-time engagement.

• Fully remote work opportunity.

• Adaptable schedule.

• Chance to increase weekly hours as project needs change.

• Project-based involvement with a potential for extension.

• Opportunity to make meaningful contributions to healthcare research and drive measurable improvements in patient outcomes.