Career Category
Information Systems
Job Description
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
.
