Data Scientist (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

Support the execution of analytics and modeling projects across therapeutic area (TA) delivery and/or investment analytics teams.

Assist in data preparation, exploratory analysis, metric tracking, and performance reporting with high accuracy.

Help maintain and operationalize dashboards or reusable code assets in Databricks or Python/SQL environments.

Contribute to project delivery by partnering with senior data scientists and leads on model building, testing, and result summaries.

Support documentation, version control, and deployment practices using standardized platforms and tools.

Stay curious — continuously learn about methods such as marketing mix modeling, predictive modeling, A/B testing, and causal inference.

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

Bachelor’s or Master’s in Data Science, Engineering, Statistics, or a related quantitative field.

3–5 years of experience in analytics, modeling, or data science delivery.

Working knowledge of Python and SQL; comfort working in Databricks or similar environments.

Experience with data visualization and performance metric tracking.

Strong attention to detail and a focus on delivering high-quality, reproducible work.

Preferred Qualifications

Exposure to marketing mix modeling, predictive modeling, or experiment-based analytics.

Interest in life sciences, healthcare, or pharma analytics environments.

Familiarity with tools such as MLflow, Git, or Jupyter notebooks.

Demonstrated ability to collaborate with analysts, engineers, or business partners to get things done.