Data Science Manager

Data Science Manager (Commercial) – L5

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

• Lead causal inference and impact measurement initiatives to evaluate the effectiveness of commercial and customer engagement strategies.
• Apply advanced statistical methods and experimentation to quantify incremental business impact and inform decision-making.
• Develop attribution frameworks across field and digital channels.
• Partner with business teams to translate commercial questions into actionable insights.
• Mentor data scientists and champion best practices in causal analytics and measurement.
  
  

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

• 6–8 years of experience in causal inference, experimentation, marketing science, commercial analytics, or data science.
• Strong expertise in statistical modeling, experimental design, and impact measurement.
• Proficiency in Python, SQL, and large-scale analytics environments.
• Ability to communicate complex analytical findings to business stakeholders.
  
  

Preferred Qualifications

• Experience in life sciences, healthcare, or pharmaceutical analytics.
• Experience with attribution, incrementality, omnichannel measurement, and marketing effectiveness.
• Familiarity with causal ML, uplift modeling, A/B testing, and quasi-experimental methods.