Engineering Manager - Commercial Data Science

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

Join Amgen’s Commercial Data & Analytics team to lead the development of scalable ML solutions for commercial decision-making.

• Serve as the internal MLOps and technical enablement partner for data science teams across TA Delivery, Measurement, and Investment Analytics.
• Lead and mentor a team of engineers, ensuring growth, skill development, and delivery of high-quality solutions
• Design, build, and maintain reusable components to support modeling, experimentation, and insight delivery at scale.
• Build robust data pipelines for ML and omnichannel promotional workflows, including data ingestion, transformation and validation
• Implement and maintain ML pipelines in Databricks, including model registration, versioning, and production deployment via MLflow.
• Validate and maintain processed data sources with schema enforcement, lineage tracking, and quality checks to ensure datasets are reliable for ML workflows.
• Support advanced commercial data science use cases such as RAG pipelines, vector DB integrations, and document-based inference with well-documented, secure code.
• Build lightweight, user-friendly interfaces (e.g., Streamlit) for visualization or prototype delivery across analytic teams.
• Collaborate with Amgen’s enterprise engineering, architecture, and cloud ops teams to follow best practices in AWS, S3, and scalable compute.
• Maintain clear documentation, version control, code reuse templates, and testing workflows to support robust and reusable deployment patterns.
• Monitor deployed models for performance drift and retraining needs; establish visibility around pipeline health and telemetry.
• Advise on connector usage, integration patterns, and technology decisions across CD&A analytics teams.

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 computer science, Software Engineering, Data Engineering, or related field.
• 7 - 13 years of experience in MLOps, data engineering, or ML/AI product development.
• Expertise in Python and SQL.
• Prior experience using deep learning frameworks, including but not limited to, PyTorch, Tensorflow, Keras, etc.
• Hands-on experience with Databricks and at least one major cloud platform (AWS, GCP, or Azure).
• Experience building reproducible ML workflows using Git-based workflows, MLflow, and Delta Lake or similar tools.Strong foundation in software engineering best practices, including CI/CD pipelines, containerization, and workflow orchestration.
• Demonstrated experience with model deployment, monitoring (drift, latency, error rates), and retraining workflows.
• Ability to clearly document technical work and collaborate effectively with cross-functional teams, including data scientists, engineers, and platform teams.
• Excellent communication skills and a willingness to communicate technical information to technical and non-technical audiences
• Agile and adaptable to a fast-paced environment

Preferred Qualifications

• Familiarity with RAG, vector databases, embedding pipelines, and LangChain-like frameworks.
• Experience with low-code interface tools like Streamlit or Dash.
• Experience optimizing ML pipelines and Spark/Databricks jobs for scale, reliability, and efficiency.
• Ability to coach analysts and DS teams on technical patterns, integrations, and deployment best practices.
• Strong interest in scaling DS enablement as a capability.
• Strong problem-solving mindset, with an eye for developing reliable and scalable data engineering systems
• Experience with pharma commercial analytics and commonly used data sources such as claims, physician level sales and promotional engagement data