Senior Scientist – Scientific Data & ML Enablement
About this opportunity
Career Category
Research
Job Description
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.
Senior Scientist – Scientific Data & ML Enablement (Large Molecule Discovery Informatics)
Live
What you will do
In this vital role, you will enable AI-driven research across Large Molecule Discovery by designing, managing, and optimizing the scientific datasets that power machine learning applications. This role will focus on transforming complex biological and experimental data into reliable, reusable, and ML-ready assets that support model development, deployment, and long-term scalability.
Working at the intersection of data engineering, scientific informatics, and machine learning, this individual will partner with scientists, AI/ML researchers, software engineers, and data platform teams to ensure that discovery data is structured and accessible for advanced analytics and AI applications. The successful candidate will help establish scalable data models, metadata frameworks, and transformation pipelines that improve data quality, consistency, and reuse across the LMD ecosystem.
This role is ideal for someone who enjoys solving complex scientific data challenges and is passionate about building the data foundations necessary to accelerate AI-enabled drug discovery.
Core responsibilities include:
• Design and maintain scalable data models supporting machine learning, analytics, and scientific research workflows
• Create and maintain ML-ready datasets for model training, validation, and deployment
• Partner with scientists to translate experimental workflows into effective data structures and reusable datasets
• Implement metadata, lineage, and governance practices that improve data quality, traceability, and reuse
• Develop standardized approaches for biological, assay, sequencing, and protein engineering datasets
• Design and optimize data transformation workflows supporting machine learning and analytics use cases
• Support integration of data from multiple scientific systems and repositories
• Collaborate with scientists, bioinformaticians, engineers, and AI teams to accelerate AI-enabled discovery research
Win
What we expect of you
Basic Qualifications:
Doctorate degree PhD with 5+ years of relevant exp
Or
Master’s degree and 8+ years of directly related experience
Or
Bachelor’s degree and 10+ years of directly related experience
Preferred Qualifications:
• Experience designing data models supporting scientific, analytical, or machine learning applications
• Strong proficiency in SQL, Python, and modern data engineering platforms
• Experience creating datasets for machine learning, predictive modeling, or advanced analytics
• Understanding of metadata, data lineage, governance, and reproducibility concepts
• Experience working with biotechnology, pharmaceutical, genomics, or life science datasets
• Familiarity with scientific data platforms and research informatics environments
• Strong analytical, communication, and cross-functional collaboration skills
Thrive
What you can expect of us
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.
Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts.
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
Stock-based long-term incentives
Award-winning time-off plans and bi-annual company-wide shutdowns
Flexible work models, including remote work arrangements, where possible
Apply now
for a career that defies imagination
Objects in your future are closer than they appear. Join us.
careers.amgen.com
Application deadline
Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.
Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
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Job details
How this role compares
Computed from every other active Drug Discovery & Preclinical Research role in our database, not just this employer's listings.
We currently track 188 comparable Senior Drug Discovery & Preclinical Research roles across 30 biopharma companies.
Salary context
62 of 188 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 188 comparable roles
+ 6 more countries
Seniority mix
188 of 188 peers have a known seniority level
Therapeutic area mix
29 of 188 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.