Sr. Data Scientist

• →Build and maintain models that identify and prioritize HCPs based on factors such as treatment behavior, patient opportunity, prescribing patterns, referral dynamics, engagement history, channel responsiveness, and likelihood to act.
• →Provide technical guidance and mentorship to data scientists, ensuring best practices in feature engineering, model development, validation, measurement, reproducibility, and business interpretation.
• →Partner with brand, sales, field operations, global analytics, data engineering, and technology teams to ensure HCP models are business-relevant, scalable, explainable, and deployable into downstream systems.

As we work to develop treatments that take care of others, we also work to care for our teammates’ professional and personal growth and well-being.

You will be part of a collaborative analytics environment where data science is used to improve commercial decision-making, strengthen customer engagement, and help teams better understand patient and HCP needs.

• 7 years of hands-on experience in predictive modeling, machine learning, and commercial analytics
• Demonstrated experience building predictive models for customer targeting, scoring, prioritization, and segmentation.
• Strong programming skills in Python and SQL, with experience working in Databricks, PySpark, or similar big data environments.
• Strong understanding of feature engineering, model validation, model performance evaluation, explainability, and business translation.
• Ability to convert ambiguous commercial questions into structured analytics problems, modeling approaches, and actionable recommendations.
• Experience with explainable ML techniques and the ability to communicate key model drivers to non-technical business stakeholders.
• Strong ownership mindset, structured problem-solving ability, and bias toward action in a fast-paced business environment.

• Experience in life sciences, pharma, biotech, or regulated healthcare analytics environments.
• Experience working with large-scale healthcare and commercial datasets, such as claims, EMR, lab, Rx, patient longitudinal data, CRM activity, sales, call activity, and digital engagement data.
• Deep understanding of HCP analytics, including targeting, prioritization, segmentation, field force optimization, omnichannel engagement, patient opportunity sizing, and treatment journey analytics.
• Experience working with IT teams to deploy models into production or business workflows
• Exposure to advanced methods such as uplift modeling, causal inference, graph/network analytics, and reinforcement learning, in commercial data science.