Lead Data Scientist, Causal Inference & Clinical Outcomes

Phamily is a leading AI-based care management company serving healthcare providers. They are seeking a Lead Data Scientist to lead a clinical outcomes study aimed at quantifying the impact of their healthcare programs and translating complex statistical findings into actionable insights for stakeholders.

Responsibilities:

• Study Design & Execution: Lead a longitudinal, quasi-experimental study starting May 7 to measure clinical outcomes, specifically hospitalization frequency and discharge follow-ups
• Causal Inference Modeling: Apply advanced methodologies (e.g., propensity score matching) to observational data to estimate counterfactual patient outcomes with minimal bias
• Actuarial-Grade Validation: Develop and refine statistical models that will be thoroughly vetted and approved by customer actuaries
• Stakeholder Management: Serve as the primary analytical face to SCMG, gathering requirements and aligning on clinical/business definitions of success
• Data Storytelling : Translate complex statistical findings into compelling presentations and client-ready reports for both technical and non-technical leadership
• Vendor Audit & Wrap-up: Extract usable value from an existing outsourced study, close out the vendor contract, and integrate relevant findings into the final study
• Technical Infrastructure: Navigate and build analytics reporting infrastructure using SQL, Python, dbt, Redshift, and Looker
• Project Handoff: Ensure all code is clean and reproducible for final handover to the internal Phamily BI team

Requirements:

• Health-Tech Expertise: Deep experience in causal inference, metric design, and clinical outcomes evaluation
• Data Proficiency: Extensive experience working with complex EHR and healthcare claims data
• Advanced Analytics Toolkit: Highly capable in Python, R, SQL, dbt, Redshift, and Looker
• Statistical Matching: Proven experience developing algorithms for high-dimensional statistical matching with large datasets
• Security Standards: Practical experience maintaining strict PHI security protocols while building data infrastructure
• Analytical Rigor: Ability to design and execute 'actuarial-grade' studies that control for significant confounding variables
• Communication: Exceptional ability to synthesize technical data into narratives for non-technical clients
• Education: Advanced degree in a quantitative field (e.g., Data Science, Statistics, Health Economics, or Epidemiology)
• Entrepreneurial DNA: A 'builder' mentality with the ability to operate effectively in high-ambiguity environments where processes may not be fully fleshed out
• HEOR Consulting: Prior background as a Health Economics & Outcomes Research (HEOR) consultant
• Actuarial Alignment: Experience in presenting methodology to and gaining approval from health system actuaries