Lead Data Scientist, Causal Inference & Clinical Outcomes

Lead Data Scientist, Causal Inference & Clinical Outcomes

New York City

Location: Remote

Job Type: Contract or Full-Time (Flexible) The project is estimated to run for 16 weeks, beginning May 7, 2026
, and concluding around August 27, 2026
.

Job

Reports To:

Director of Business Intelligence

Salary Range: Contract rate is $100 - $160/hour

Jaan Health is a leading AI-based care management company serving healthcare providers. For nearly a decade, the company has leveraged its easy-to-use, proprietary technology to enable health systems, medical groups, and ACOs to deliver high-quality, high-ROI proactive care to hundreds of thousands of previously underserved patients.

Phamily, the company's core technology platform, has transformed chronic disease management with clinically tested AI and easy-to-use technology that enables physicians and care teams to offer high-touch, individualized patient care that has been proven to reduce investment in extra labor and the overall cost of care. Phamily helps ensure healthcare providers are compensated fairly for providing high-quality care between office visits, while improving the lives of patients with chronic diseases.

Learn more at

We are seeking an analytical powerhouse to redefine proactive healthcare as our Lead Data Scientist for Causal Inference & Clinical Outcomes
. In this 16-week engagement starting May 7, 2026
, you will lead a rigorous clinical outcomes study for our key client, Silver Cross Medical Group (SCMG), to quantify the impact of our Advanced Primary Care Management (APCM) and Chronic Care Management (CCM) programs.

Your primary mission is to provide empirical evidence answering two critical questions:
Do patients in our programs have lower hospital readmission rates and higher discharge follow-up rates?. This role requires a specialist who can manipulate complex healthcare claims and EHR data to build an airtight, actuarial-grade causal inference framework. Additionally, you will audit and extract value from a previous analytics vendor’s deliverables to close out their engagement. If you are a health-tech veteran who excels at translating complex statistical findings into compelling narratives for stakeholders, we want to hear from you.

• 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.

• 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.

• This is a remote-first position.
• The project is estimated to run for 16 weeks, beginning
  May 7, 2026
  , and concluding around August 27, 2026
  .
• Competitive compensation…