Data Science Manager

**HOW MIGHT YOU DEFY IMAGINATION?** If you feel like you're part of something bigger, it's because you are. At Amgen, our shared mission-to serve patients-drives all that we do. It is key to our becoming one of the world's leading biotechnology companies. We are global collaborators who achieve together-researching, manufacturing, and delivering ever-better products that reach over 10 million patients worldwide. It's time for a career you can be proud of. **Live | What you will do** + 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. **Thrive | What you can expect** 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. **Basic Qualifications** + 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. **Preferred Qualifications** + 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.