Data Science Manager (Commercial)

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

• Provide technical guidance and mentorship to data scientists, ensuring best practices in modeling, measurement, and reproducibility.
• Design and build predictive models to support patient analytics, campaign measurement, and recommendation use cases such as alerts, triggers, targeting, and next-best-action.
• Lead the design of pilots, A/B tests, and analytic frameworks to drive test-and-learn strategies.
• Translate complex model outputs into clear business-facing summaries to enable decision-making by commercial and brand teams.
• Develop, validate, and maintain machine learning models using Python, PySpark, and Databricks across large longitudinal datasets.
• Maintain best practices in model versioning, QC, documentation, and reproducibility using MLflow and Git.
• Collaborate with brand partners, global analytics, and engineering teams to ensure seamless model deployment and interpretation.
• Drive innovation by exploring emerging techniques (e.g., causal inference, reinforcement learning, RAG/LLMs) for commercial data science applications.

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.

Basic Qualifications

• Master’s degree in Data Science, Computer Science, Public Health, or related field.
• 7–13 years of hands-on experience in predictive modeling, machine learning, or healthcare analytics.
• Strong programming skills in Python and SQL, with experience using Databricks or similar platforms.
• Solid grasp of experimentation methods including causal inference, uplift modeling, and A/B testing.
• Experience working with patient-level or longitudinal healthcare data (e.g., claims, EMR, lab).

Preferred Qualifications

• Critical thinking and structured problem-solving ability to intake ambiguous business requirements and develop analytics frameworks.
• Experience in life sciences, pharma, or regulated health analytics environments.
• Familiarity with MLflow, AWS, Git, and DevOps-style model deployment workflows.
• Understanding of patient journey analytics, including HCP targeting or patient triggers.
• Exposure to RAG/LLM applications in data science.
• Strong communication skills and ability to work across technical and business stakeholders.
• Strong ownership of work with a bias towards action.