Senior Applied Data Scientist, Pharma

Title: Senior Applied Data Scientist, Pharma

Location:

Boston, MA; New York, NY; Chicago, IL; Texas

Duration: Full-Time

Visa sponsorship:

Not open to any visas (US citizens and Green Card holders only)

About the Role

Own commercial pharma accounts end to end. You design and deploy predictive models that help big pharma field force and sales teams target the right healthcare providers (HCPs) and communicate more effectively with doctors about their therapeutics. You are the data expert in the room, combining client data with 1.2+ petabytes of proprietary data to drive measurable commercial outcomes.

3. Responsibilities

• Own commercial pharma accounts end to end, from data integration and study design through production models, pipelines, and ongoing optimization
• Build predictive models for HCP targeting and pharma marketing use cases (segmentation, propensity modeling, campaign optimization)
• Design and defend commercially relevant studies, such as precision targeting to accelerate patient recruitment for Phase 2/3 trials and speed time-to-market for therapeutics
• Deploy and operate models in production on the cloud (AWS/GCP), including MLOps and monitoring
• Work directly with pharma commercial, analytics, and IT stakeholders, translating business problems into data-driven solutions
• Feed patterns and high-leverage requirements across accounts back into product roadmap
• Run 3–5 major health system deployments from pilot design through production, shaping how predictive models drive real patient outcomes and influencing the product roadmap

4.

Requirements

Key requirements

• Pharma industry experience, ideally on the commercial side of big pharma (HCP targeting, field force / sales enablement). Broader pharma or biopharma data science backgrounds (e.g., Tempus AI) are also considered.
• Hands-on experience designing, delivering, and putting predictive models into production end to end
• Fluent in Python and SQL, with cloud experience (AWS, GCP)
• Strong client-facing skills: able to run working sessions with senior stakeholders and defend your methodology
• Entrepreneurial mindset: comfortable wearing multiple hats in a lean, fast-paced environment (startup or big-company backgrounds welcome)

Nice to have

• Experience in rare disease or specialty launch
• Balancing the need for startup experience with the ability to scale beyond $100M in revenue

Tech stack:

Python, pandas, scikit-learn, Airflow, Spark, PySpark, AW[S] (the source text is cut off)

5. Dealbreakers, Nice-to-Haves, Traits to Avoid

Dealbreakers

• Work experience:

Commercial big-pharma experience (HCP targeting, field-force enablement, commercial/marketing analytics). Biopharma like Tempus AI is OK. Pure clinical/R&D is weaker.

(Updated)

• Work experience:

Owns client technical delivery end to end: pre-sales scoping, architecture, build, and post-sale integration (solutions eng/architecture, production eng, or technical consulting). (Updated)

• Soft skills:

Client-facing communication and professional presence (running technical sessions, presenting to clients). (Baseline)

• Seniority:

10+ years of experience as an applied data scientist

• Hard skills:

Experience building data and ML pipelines in Python/TypeScript (PySpark, ETL/ELT, unstructured data, document ingestion, predictive models). (Updated)

Nice-to-have

• Work experience:

Founding engineer, startup co-founder, startup CTO, or software engineer at a high-growth startup. (Updated)

• Work experience:

Predictive modeling on commercial pharma use cases (HCP targeting, segmentation, propensity, campaign optimization). Rare disease or specialty launch a plus. (Updated)

• Hard skills:

Experience building LLM systems or AI agents (prompt engineering, function calling, structured output, guardrails, observability). (Updated)