Lead Data Scientist
Location
United States
Posted
27 days ago
Salary
0
Seniority
Senior
Job Description
Lead Data Scientist
Dermsquared
• Shape the data science roadmap: Identify measurement, audience, and AI capabilities that create value in each specialty market, and build a function that scales with the business. • Build causal lift measurement: Own the framework that demonstrates how HCP engagement drives real-world prescribing change — matched and synthetic controls, lookalike populations, methodologies defensible to pharmaceutical medical affairs and legal, and repeatable across therapeutic areas. • Build decisioning and personalization systems: Develop next-best-action, recommendation, and audience intelligence models that personalize the HCP experience and identify high-value audiences for sponsor targeting at scale. • Apply LLMs and generative AI across the workflow: Use them for structured extraction, feature engineering, insight generation, and HCP profile enrichment — wherever they cut time-to-insight without compromising rigor. • Translate findings into commercial products: Turn model outputs into sponsor-facing measurement reports, audience intelligence packages, and ROI dashboards, and present methodology directly to pharmaceutical medical affairs and commercial teams. • Own the ML platform layer: Feature engineering, experiment tracking, model registry, A/B and holdout frameworks, and production monitoring on the company's cloud and data warehouse stack.
Job Requirements
- Demonstrated leadership essentials as described above
- 4-year degree from an accredited academic institution
- 5+ years in applied data science, ML engineering, or a closely related quantitative field, with production models deployed — not just research prototypes
- Production-quality Python and advanced SQL; hands-on experience with a modern cloud data warehouse at scale (e.g., Snowflake, BigQuery, Databricks)
- Experience deploying models on a major cloud ML platform (e.g., AWS SageMaker, GCP Vertex AI, Azure ML) and working with modern ML tooling — feature stores, experiment tracking, model registries, A/B testing, and production monitoring
- Hands-on experience applying LLMs and generative AI — prompt engineering, structured extraction, RAG, or LLM-assisted feature generation — with judgment about when LLMs add value vs. when classical methods do
- Strong causal inference and experimental design background — matched and synthetic controls, difference-in-differences, propensity score matching, instrumental variables
- Experience building recommendation, next-best-action, or audience scoring systems at scale using behavioral and third-party data
- Able to communicate statistical methodology and model behavior clearly to non-technical audiences, including commercial and medical affairs stakeholders
- Healthcare, life sciences, pharma analytics, or digital health experience strongly preferred; experience with real-world claims or HCP data, or scaling capabilities across multiple markets, is a plus
Benefits
- Fully remote work environment with a flexible time off policy
- Dedicated professional development support, including reimbursement for approved learning and development activities.
- Comprehensive wellness benefits, including medical, vision, and dental coverage, with a significant portion of premiums subsidized for employees and eligible dependents, as well as access to HSA, FSA, and Dependent Care FSA plans
- Employer-paid life insurance, short-term disability, and long-term disability coverage
- 401(k) retirement plan with company matching
- Cell phone reimbursement for business use
- Home office stipend to support a productive remote setup
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