ML / Data Engineer – Data Science Enablement

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. We are global collaborators who achieve together—researching, manufacturing, and delivering ever-better products that reach millions of patients worldwide. It’s time for a career you can be proud of.

Role Overview

This role will report to the Data Science Enablement Manager and support the Rare Disease Business Unit (RDBU) patient finding team. The candidate will work closely with data scientists to build scalable pipelines, productionize models, and establish robust evaluation and monitoring frameworks to enable reliable, high-impact deployment of patient finding solutions.

Live | What you will do

• Build and maintain scalable data and ML pipelines to support patient finding use cases across the patient journey
• Productionize machine learning models by developing deployment workflows, APIs, and batch/real-time scoring pipelines
• Design and implement model evaluation, validation, and monitoring frameworks (performance tracking, drift detection, alerting)
• Enable end-to-end ML lifecycle management, including training, versioning, deployment, and retraining workflows
• Partner with RDBU data science teams to translate analytical solutions into production-ready systems
• Develop ML-ready datasets and feature pipelines, ensuring data quality, consistency, and reusability
• Support model tracking and experiment management using standardized tools and frameworks
• Build tools and utilities to monitor, track, and operationalize model outputs for downstream consumption
• Collaborate with enterprise data and platform teams to ensure compliance with data governance, security, and architecture standards
• Follow engineering best practices for code quality, documentation, testing, and CI/CD integration

Thrive | What you can expect

• Work on productionalizing advanced patient finding models in a rare disease context
• Exposure to end-to-end ML systems and real-world deployment challenges
• Close collaboration with data scientists on high-impact commercial use cases
• Opportunity to shape ML engineering and enablement standards at scale

Basic Qualifications

• Bachelor’s or Master’s in Computer Science, Data Engineering, or related technical field
• 3–5 years of experience in ML engineering, data engineering, or related roles
• Strong programming skills in Python and SQL
• Experience with data pipeline development and distributed computing (e.g., Spark/PySpark)
• Working knowledge of Databricks and at least one cloud platform (AWS, Azure, or GCP)
• Experience with ML lifecycle tools (e.g., MLflow, Git, CI/CD pipelines)
• Understanding of model deployment, monitoring, and reproducibility practices

Preferred Qualifications

• Experience supporting production ML systems in healthcare or commercial analytics contexts
• Familiarity with model monitoring concepts (data drift, model decay, performance tracking)
• Experience building feature stores or reusable data assets
• Exposure to patient journey or patient finding use cases is a plus
• Experience with containerization and orchestration frameworks
• Strong collaboration skills and ability to work closely with data science and analytics teams
• Passion for building scalable systems and enabling data science teams