This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
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.
Job Responsibility:
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
Requirements:
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
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 frameworks)
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
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
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
Nice to have:
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