Lead Data Scientist

Patient-Level Data Analysis : Work hands-on with anonymized patient-level data (APLD) and prescriber / account level datasets (e.g. Xponent, Drug Distribution Data) to extract insights, ensuring data quality and integrity while integrating data from diverse healthcare sources.

Cohort Identification & Tracking : Develop and refine business rules to identify eligible patient / prescriber cohorts and implement methodologies for longitudinal tracking of these cohorts over time.

Predictive Modeling :

Develop prescriber / patient look-a-like models to support targeting and sizing exercises.

Develop promotional response, clustering / segmentation models to evaluate effectiveness of strategic decisions.

Build, test, and deploy time series forecasting models to predict trends in HCP prescribing patterns.

Model Documentation & Knowledge Sharing : Maintain thorough documentation of data analysis processes, model development, and insights. Develop clear presentations and reports to effectively communicate findings to team members.

Cross-Functional Collaboration : Partner with data engineers, Insights & Analytics teams, and external stakeholders to align analytical solutions with organizational goals. Support ad-hoc analytical requests and contribute to strategic data initiatives.

Analytical Reporting & Dashboarding : Create and maintain dynamic dashboards and reports in traditional BI tools (e.g. Power BI, Tableau) and GenAI driven tools (e.g. Databricks Genie) to translate complex data findings into actionable business insights for commercial stakeholders.

Innovation & Continuous Improvement : Explore and evaluate emerging analytical techniques, including GenAI capabilities where applicable, to enhance existing methodologies and drive innovation in patient analytics.