Scientist, Biomarker Analysis

Location: India - Hyderabad (AIN) Work Location Type: On-site Organization: Global Development - Precision Medicine People Leadership: No Scope: Develop and deliver superior clinical biomarker analytics to Amgen clinical teams across the portfolio. Join Amgens Mission of Serving PatientsAt 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 SummaryThe Biomarker Analysis Scientist will play a critical role in advancing translational and reverse-translational insights from clinical trial data across Amgens 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 Amgens India site in Hyderabad and operates as part of a globally integrated Precision Medicine organization. Key ResponsibilitiesDesign, 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 benefitrisk 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 QualificationsDoctorate degree with 3+ years of relevant scientific experience ORMasters degree with 5+ years of relevant scientific experience ORBachelors degree with 7+ years of relevant scientific experiencePreferred QualificationsCandidates 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 institutionDemonstrated experience analyzing complex, large-scale biological and clinical datasets, including multi-modal and longitudinal dataStrong 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 .