Principal Data Scientist - Pharma Commercial Analytics

• Develop patient- and HCP-level models to support patient identification, treatment switching, and therapy persistence.
• Build predictive models and analytics solutions to drive next-best-action recommendations and multichannel engagement strategies.
• Design and evaluate experiments, including holdout groups and lift measurement, to assess the effectiveness of commercial campaigns.
• Apply explainable machine learning techniques, including gradient boosting and SHAP, to improve model transparency and interpretability.
• Analyze US healthcare claims, real-world data (RWD), CRM, and promotional data to generate actionable business insights.
• Work with large datasets using Python, SQL, and platforms such as Databricks or Snowflake.
• Lead a client-facing data science team, ensuring high-quality delivery and alignment with business objectives.
• Present analytical findings, model outcomes, and strategic recommendations to brand leadership and other senior stakeholders.
• Collaborate with cross-functional teams to translate business challenges into scalable data science solutions.

We are looking for a Lead Data Scientist with strong experience in pharmaceutical commercial analytics, patient-level modeling, and healthcare data science. The ideal candidate will have hands-on experience working with US claims and real-world data (RWD) to develop predictive models, generate actionable insights, and optimize HCP engagement and commercial strategies.

The role requires strong technical expertise, leadership capabilities, and the ability to work directly with clients and present analytical findings to senior stakeholders., + Strong experience in pharmaceutical commercial analytics and healthcare data science.

• Hands-on experience working with US healthcare claims and real-world data, using platforms or providers such as IQVIA, Komodo Health, or PurpleLab.
• Experience with Veeva CRM data and pharmaceutical promotional data.
• Expertise in patient-level and HCP-level modeling, including patient identification, treatment switching, and persistence.
• Experience developing next-best-action models and multichannel analytics solutions.
• Strong knowledge of experimental design, holdout strategies, and campaign lift measurement.
• Proficiency in Python and SQL, with experience using Databricks or Snowflake.
• Experience with gradient boosting techniques and explainable AI methods such as SHAP.
• Proven ability to lead data science teams, manage client relationships, and communicate insights to senior stakeholders.

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

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