Join Amgen’s Mission of Serving Patients

At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.

Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas -Oncology, Inflammation, General Medicine, and Rare Disease
- we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Our award-winning culture is collaborative, creative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

Data Scientist (US Value & Access Insights)

What you will do

Let’s do this. Let’s change the world.



In this vital role you will be responsible for developing and deploying advanced machine learning, operational research, semantic analysis, and statistical methods to uncover structure in large data sets. This role involves creating analytics solutions to address customer needs and opportunities.

Roles & Responsibilities:
- Ability to work on upgrades and manage the execution of Proprietary AI engine built to optimize Copay and other GTN initiatives
- Ensure models are trained with the latest data and meet the SLA expectations
- Work with a global cross functional team on the AI tool’s road map
- Perform exploratory and targeted data analyses using descriptive statistics and other methods.
- Model/analytics experiment and development pipeline leveraging MLOps.
- Collaborate with technical teams to translate the business needs into technical specifications, particularly focusing on AI-driven automation and insights.

What we expect of you

We

📌 Data Scientist (Hyderabad)
🏢 AMGEN
📍 Hyderabad