Scientist, Biomarker Analysis
Join Amgen’s Mission of Serving Patients At
Amgen, our mission to serve patients living with serious illnesses
drives everything we do. As Amgen expands its technology and innovation
presence in Hyderabad, this role will be pivotal in delivering
high-quality translational analytics to accelerates drug development
through rigorous science, data, and responsible AI.
Role Summary The
Biomarker Analysis Scientist will play a critical role in advancing
translational and reverse-translational insights from clinical trial
data across Amgen’s global portfolio, including Oncology, Inflammation,
Rare Disease, Cardiovascular & Metabolic, and Obesity & Related
Diseases. This role is embedded within the Computational Biology and
Translational Analytics function in Precision Medicine and is expected
to operate with a high degree of scientific independence, technical
depth, and cross-functional influence.
The
successful candidate will be experienced with analytics within the drug
development lifecycle and will design and execute rigorous biomarker
and translational analyses using complex, high-dimensional clinical
datasets, integrating multi-omics, imaging, and clinical metadata to
support decision-making across early and late-stage development
programs. This position requires strong biological intuition, advanced
quantitative expertise, and the ability to communicate clearly and
effectively with global, matrixed stakeholders.
This
role is based at Amgen’s India site in Hyderabad and operates as part
of a globally integrated Precision Medicine organization.
Key Responsibilities
Design,
execute, and interpret biomarker and translational analyses to support
clinical development programs, including target engagement &
stratification, pharmacodynamic modeling, patient stratification,
mechanism of action validation, indication selection, and benefit–risk
assessments.
Develop and apply
robust analytical workflows for high-content, multi-modal clinical data,
including bulk and single-cell genomics, transcriptomics, proteomics,
metabolomics, epigenomics, spatial omics, imaging, and emerging assay
modalities.
Translate complex
biological and clinical questions into quantitative analysis plans,
statistical models, and computational frameworks that generate
actionable insights.
Integrate
internal clinical trial data with external datasets (e.g., public omics
resources, real-world data, literature-derived knowledge) to
contextualize findings and inform program strategy.
Contribute
to portfolio-level analyses and cross-asset learnings through
principled data mining, visualization, and knowledge discovery
approaches.
Partner closely with
biologists, clinicians, assay scientists, and data engineering teams to
ensure analytical rigor, data quality, and scientific relevance.
Clearly
communicate analytical approaches, assumptions, limitations, and
conclusions to diverse audiences through written reports, presentations,
and cross-functional forums.
Operate
effectively in a global, matrixed environment, including regular
collaboration across time zones with US- and EU-based teams.
Strategically leverage AI to enhance speed, accuracy and insightfulness
of results, maximally integrating relevant findings in the public
domain.
Basic Qualifications Doctorate
degree with 3+ years of relevant scientific experience OR Master’s
degree with 5+ years of relevant scientific experience OR Bachelor’s
degree with 7+ years of relevant scientific experience
Preferred Qualifications
Candidates are expected to demonstrate most of the following:
Scientific & Technical Expertise
PhD
(or equivalent) in Bioinformatics, Computational Biology, Statistics,
Applied Mathematics, Computer Science, Data Science, or a closely
related quantitative discipline from a well-regarded institution
Demonstrated
experience analyzing complex, large-scale biological and clinical
datasets, including multi-modal and longitudinal data
Strong
grounding in statistical modeling and methods (e.g., regression,
mixed-effects models, multivariate methods, correlative and causal
analysis, prognostic and predictive biomarker analysis farmeworks) and
experience applying these methods in a translational or clinical
context.
Working knowledge of
machine learning and AI methodologies, with practical experience
applying them to biological or clinical data; experience in clinical
trial settings is strongly preferred.
Familiarity
with clinical biomarker platforms and data types, such as NGS, flow
cytometry, IHC, immunoassays, imaging, and transcriptional profiling.
Proficiency
in R and Python and version control (e.g. gitlab), with evidence of
writing clear, reproducible, and maintainable analytical code;
familiarity with modern data science ecosystems (e.g., tidyverse in R
and equivalent libraries in python) and best practices in reproducible
research.
Translational Impact & Industry Experience
Proven ability to connect molecular-level findings to clinical hypotheses and development decisions.
Experience supporting drug development programs in a biotech or pharmaceutical setting (typically 3+ years).
Working knowledge of assay development, validation, and qualification considerations for clinical trial support.
Evidence
of independent scientific contribution through peer-reviewed
publications in reputable journals. Collaboration, Communication &
Global Mindset
Excellent
written and spoken English communication skills, with the ability to
explain complex analyses clearly to non-computational stakeholders.
Demonstrated experience working effectively with global teams and stakeholders across geographies and time zones.
Willingness and ability to operate flexibly across time zones to support global programs.
Strong
interpersonal skills characterized by intellectual humility,
adaptability, curiosity, and a proactive approach to collaboration.
Ability
to think critically and creatively, ask clarifying questions, challenge
assumptions constructively, and pivot analytical approaches as program
needs evolve.
What Differentiates Top
Candidates Clear evidence of end-to-end ownership of translational or
biomarker analyses in clinical programs. Strong applied statistical
background.
Demonstrated impact on development decisions rather than purely methodological contributions.
A track record of thriving in complex, ambiguous environments and driving alignment across scientific and technical teams.
Experience working in or with large, global pharmaceutical organizations.
This
position offers the opportunity to contribute meaningfully to Amgen’s
global development portfolio while helping to establish and grow a
high-impact Precision Medicine capability at Amgen India.
