Scientist – Digital Discovery: Biological Data Systems & Machine Learning

Career Category  Scientific  

Job Description  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. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity‑related conditions.

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, innovative, 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.
Scientist – Digital Discovery:
Biological Data Systems & Machine Learning  What you will do  Let’s do this. Let’s change the world. In this vital role, we are seeking a Scientist, Biological Data Systems & Machine Learning to join the Digital Discovery team. This role sits at the intersection of wet‑lab experimental biology, data systems, and machine learning, enabling a closed‑loop discovery engine where data generation, structuring, and modeling continuously inform one another.
Scientific Data Architecture & Modeling   Define and implement data models, schemas, and relationships for biological data
Ensure robust data lineage, metadata standards, and interoperability across systems
Establish best practices for ML‑ready biological datasets
Legacy Data Mining & Curation for AI/ML   Identify, access, and harmonize proprietary legacy discovery datasets
Perform data archaeology to reconstruct experimental context and metadata
Build high‑quality, ML‑ready training datasets for model development
Partner with AI/ML teams on data requirements and dataset design
Experiment–Data–Platform Integration   Translate experimental workflows into digital systems (e.g., Benchling)
Define requirements for workflows, entities, and dashboards with engineering teams
Ensure data is captured in a structured, future‑ready manner
Computational Analysis & ML Enablement   Analyze large‑scale biological datasets to generate insights
Support development of predictive and generative ML models
Optimize dataset structure and feature engineering
Cross‑Functional Integration   Interface across experimental biology, AI/ML, data engineering, and business teams
Translate scientific needs into technical requirements and vice versa
Align workflows with enterprise data ecosystem strategies
Workflow Optimization & Automation   Identify inefficiencies and design scalable data workflows
Develop tools and dashboards to improve data accessibility and usability
Ensure robustness and integrity of datasets and tools
Identify edge cases and prevent downstream issues
Adoption & Enablement   Drive adoption of data platforms and best practices
Serve as a trusted advisor to scientists on data standards and workflows
What we expect of you  We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a Scientist with these qualifications.

Basic Qualifications:

PhD in Biology, Immunology, Immunoengineering, Biochemistry, Bioengineering or related field
OR Master’s degree + 3+ years of relevant experience
OR Bachelor’s degree + 5+ years of relevant experience

Preferred Qualifications:

Training in Bioinformatics, or related field
Strong background in wet‑lab biology

Experience with large‑scale biological datasets

Experience with data modeling, curation, and ETL pipelines
Proficiency in Python and/or R

Experience with machine learning approaches
Familiarity with scientific data platforms (e.g., Benchling)
Experience working cross‑functionally across science, data, and engineering teams
What you can…