Senior Data Scientist - Healthcare/Pharmaceutical Experience

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Data Scientist

Location: 100% remote

Duration: 6 months CTH

Must be able to convert to full time without requiring sponsorship.

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Pay rate-$55-60/hr on W2

We are looking for a senior level Data Scientist with healthcare/pharmaceutical experience, strong SQL skills, and experience modeling, targeting and optimizing communication channels.

Qual notes:

Ad Hoc Data Insights and Agentic Models: The team''s focus on ad hoc data requests and ''off the shelf'' insights, including the use of agentic models and the importance of familiarity with large language models (LLMs) and evaluation approaches for packaging data to specific sectors.

Metabolic Bundle Project Staffing: Focusing on the metabolic bundle sold through PBM to insurers and employers and outlined the requirement for a data scientist with product experience to handle modeling for targeting and strategy at a senior level, due to a reduced team size and increased demand.

Role Focus and Responsibilities: Expected responsibilities for the new data scientist, emphasizing a balance between analysis, insights, modeling for targeting, optimizing communication channel preferences, and supporting evaluation work, with prioritization based on current team strengths.

Team Structure and Support Needs: Assess and communicate the current team structure and support gaps due to the principal data scientist''s reprioritization. Head count was lost – high demand for work, team smaller than they need to be.

The Metabolic Health Data Scientist applies advanced analytics, statistical modeling, and machine learning to improve outcomes across cardiometabolic conditions such as diabetes, obesity, and related comorbidities. This role partners closely with product, clinical, and engineering teams to design, develop, and scale data‑driven solutions that enable personalized interventions, close care gaps, and demonstrate measurable clinical and financial impact.

The Data Scientist is responsible for translating complex clinical and behavioral data into actionable insights, predictive models, and performance metrics that support evidence‑based decision‑making. By leveraging diverse data sources—including medical claims, labs (e.g., A1c), digital engagement, and program operations—the role helps evolve metabolic health programs from rules‑based approaches to intelligent, outcomes‑oriented platforms aligned with clinical guidelines and business objectives.