Lead Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Define the data science strategy: Identify metrics, audience insights, and AI capabilities that generate value across each specialty market, and establish a scalable function that aligns with business growth.

• Develop causal lift measurement: Manage the framework that illustrates how HCP engagement influences real-world prescribing changes — including matched and synthetic controls, lookalike populations, and methodologies that are defensible to pharmaceutical medical affairs and legal, and are repeatable across therapeutic areas.

• Create decision-making and personalization systems: Build next-best-action, recommendation, and audience intelligence models that tailor the HCP experience and pinpoint high-value audiences for targeted sponsorship on a large scale.

• Integrate LLMs and generative AI into workflows: Utilize these technologies for structured data extraction, feature engineering, insight generation, and HCP profile enhancement — maximizing efficiency without sacrificing rigor.

• Convert insights into commercial products: Transform model outputs into sponsor-facing measurement reports, audience intelligence packages, and ROI dashboards, presenting methodologies directly to pharmaceutical medical affairs and commercial teams.

• Oversee the ML platform layer: Manage feature engineering, experiment tracking, model registry, A/B testing, and production monitoring on the organization’s cloud and data warehouse infrastructure.

⛳️ Requirements

• Proven leadership skills as outlined above.

• Bachelor’s degree from an accredited institution.

• Over 5 years of experience in applied data science, ML engineering, or a closely related quantitative field, with production models in deployment — not just research prototypes.

• Proficient in production-quality Python and advanced SQL; practical experience with a modern cloud data warehouse at scale (e.g., Snowflake, BigQuery, Databricks).

• Experience deploying models on a leading cloud ML platform (e.g., AWS SageMaker, GCP Vertex AI, Azure ML) and working with contemporary ML tools — feature stores, experiment tracking, model registries, A/B testing, and production monitoring.

• Hands-on experience with LLMs and generative AI — including prompt engineering, structured extraction, RAG, or LLM-assisted feature generation — with discernment about when LLMs are advantageous versus classical methods.

• Strong expertise in causal inference and experimental design — matched and synthetic controls, difference-in-differences, propensity score matching, instrumental variables.

• Experience in building recommendation, next-best-action, or audience scoring systems at scale using behavioral and third-party data.

• Capable of clearly communicating statistical methodologies and model behaviors to non-technical audiences, including stakeholders in commercial and medical affairs.

• Experience in healthcare, life sciences, pharmaceutical analytics, or digital health is highly preferred; familiarity with real-world claims or HCP data, or scaling capabilities across various markets, is a plus.

🏝️ Benefits

• Fully remote work setup with a flexible time-off policy.

• Dedicated support for professional development, including reimbursement for approved learning and development activities.

• Comprehensive wellness benefits covering medical, vision, and dental, with a substantial 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-related use.

• Home office stipend to facilitate a productive remote working environment.