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.

Large Molecule Ontology Specialist (Large Molecule Discovery Informatics)

Live

What you will do

Let’s do this. Let’s change the world. In this vital and exciting role, you will
serve as a Large Molecule Ontology Specialist within the Research organization,
driving the development and implementation of ontology, metadata, and semantic
data frameworks that enable scalable AI, advanced analytics, and
cross-functional scientific collaboration.

You will be responsible for defining and implementing assay and metadata
standards across Large Molecule Discovery (LMD),



ensuring semantic consistency
and interoperability across experimental platforms, scientific workflows, and
data modalities. The successful candidate will partner closely with research
scientists, informaticians, data engineers, and enterprise data governance teams
to establish harmonized ontologies, metadata frameworks, and FAIR data practices
that improve data discoverability, reuse, and AI readiness.

Operating at the intersection of biology, data science, and informatics, you
will combine scientific domain expertise with a deep understanding of semantic
technologies, ontology management, and scientific data standards to build
foundational capabilities that accelerate discovery.

In this role, your core responsibilities include:

* Develop and maintain LMD ontology frameworks, semantic standards, and
controlled vocabularies across key discovery workflows and datasets
* Align ontologies with enterprise data stand

📌 Sr Scientist (Hyderabad)
🏢 AMGEN
📍 Hyderabad