Lead Data Scientist, Causal Inference, Clinical Outcomes

This is a fully remote position, open to applicants in New York.

📋 Description

• Design & Execute Studies: Lead a longitudinal, quasi-experimental research initiative commencing on May 7 to assess clinical outcomes, particularly focusing on hospitalization rates and discharge follow-ups.

• Causal Inference Techniques: Utilize advanced methods (such as propensity score matching) on observational datasets to estimate counterfactual patient outcomes while minimizing bias.

• Actuarial Validation: Create and enhance statistical models that will undergo comprehensive evaluation and approval by customer actuaries.

• Stakeholder Engagement: Act as the primary analytical liaison to SCMG, gathering requirements and ensuring alignment on clinical and business success definitions.

• Data Visualization: Convert intricate statistical results into engaging presentations and client-ready reports for both technical and non-technical stakeholders.

• Vendor Review & Conclusion: Derive actionable insights from an existing outsourced study, finalize the vendor contract, and incorporate relevant findings into the conclusive study.

• Technical Framework: Develop and manage analytics reporting infrastructure utilizing SQL, Python, dbt, Redshift, and Looker.

• Project Transition: Guarantee that all code is clean and reproducible for the final transfer to the internal Phamily BI team.

⛳️ Requirements

• Expertise in Health-Tech: Extensive experience in causal inference, metric design, and evaluation of clinical outcomes.

• Data Management Skills: Significant experience working with complex electronic health records (EHR) and healthcare claims data.

• Advanced Analytical Skills: Proficient in Python, R, SQL, dbt, Redshift, and Looker.

• Statistical Matching Experience: Demonstrated ability in developing algorithms for high-dimensional statistical matching with extensive datasets.

• Security Compliance: Practical experience in upholding stringent PHI security protocols while constructing data infrastructure.

• Analytical Precision: Capability to design and implement 'actuarial-grade' studies that account for major confounding variables.

• Strong Communication: Exceptional skill in synthesizing technical data into narratives understandable by non-technical clients.

• Educational Background: Advanced degree in a quantitative discipline (e.g., Data Science, Statistics, Health Economics, or Epidemiology).

🏝️ Benefits

• Competitive compensation reflective of experience

• Opportunity to earn equity based on performance

• Medical, dental, and vision coverage available for employees and dependents at a minimal cost

• Paid maternity leave

• Flexible Spending Account (FSA) and Dependent Care account options

• Eligibility for 401(k) after 6 months of full-time employment

• Collaborative, mission-driven work atmosphere