Senior Scientist - Computational Discovery & Translational Analytics

Amgen • South San Francisco, CA

21h

About The Position

Join Amgen’s Mission of Serving Patients At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career. Senior Scientist - Computational Discovery & Translational Analytics What you will do Let’s do this. Let’s change the world. We are seeking a highly qualified and motivated Senior Scientist with a strong background in computational biology to join the Bioinformatics Technologies team within Amgen’s Automation, Research Data Systems, Informatics, and AI (ARIA) organization. ARIA is a multidisciplinary group embedded within Amgen’s discovery engine, leveraging advancements in digital technologies for disease modeling and digital modality engineering to accelerate the pipeline from target inception through drug development. Within ARIA, Bioinformatics Technologies serves as an innovation hub for developing, deploying, and scaling emerging computational technologies to drive the next generation of target and therapeutic discovery. This role focuses on developing and applying advanced computational approaches to enable target discovery, validation, and biomarker identification across multimodal biological datasets. You will operate at the intersection of surfaceome biology, perturbation screening, non-coding variant interpretation, and disease tissue profiling, contributing directly to biological insight generation and data-driven decision frameworks that support preclinical drug discovery. The successful candidate will combine strong analytical rigor with deep technical expertise in computational biology, including modern statistical and machine learning approaches, and a solid foundation in molecular biology. A demonstrated track record of innovative, collaborative research and the ability to translate complex data into actionable biological insights is essential.

Requirements

• PhD in computational biology, bioinformatics, statistics, computer science, data science, or a related quantitative discipline [and relevant post-doc where applicable]
• Master’s degree and 3 years of directly related experience
• Bachelor’s degree and 5 years of directly related experience

Nice To Haves

• Strong foundation in computational biology methods, including statistical modeling and AI/ML, with the ability to apply these approaches to high-dimensional biological datasets.
• Experience working with large-scale biological data, including long-read RNA sequencing, single-cell multi-omics, spatial omics, perturbation screens (e.g., Perturb-seq), translatomics (e.g., Ribo-Seq), proteomics, and/or epigenomic data (e.g., Hi-C, ATAC-seq).
• Proven ability to develop novel computational methods that generate tractable biological insights from integrating complex datasets, balancing deep methodological innovation within individual data modalities with holistic integration across multimodal datasets, and translating results into testable hypotheses that inform target discovery, validation, or biomarker development.
• Familiarity with disease tissue biology and the ability to interpret data in a biologically meaningful context, including cellular heterogeneity and disease-relevant states.
• Strong programming skills in Python, R, or similar languages, with experience developing reproducible and well-documented analytical workflows.
• Creative, open-minded, and passionate about research, with a proven record of innovative algorithm and model development demonstrated through impactful publications, patents, or widely adopted tools.
• Excellent communication and collaboration skills, with the ability to thrive across computational, experimental, and translational teams and effectively present results to diverse audiences.

Responsibilities

• Develop and apply advanced computational methods to enable target discovery, validation, and biomarker identification across multimodal datasets, with a focus on surfaceome biology, perturbation screening, and variant-to-gene-to-function.
• Integrate diverse biological data types, including bulk, single-cell, and spatial omics, perturbation datasets, and genetic and epigenomic data, to generate insights into disease mechanisms, therapeutic hypotheses, and disease tissue context across therapeutic areas.
• Identify cell-type-specific and disease-relevant targets and characterize their cellular context, heterogeneity, and activity in disease-relevant states through integrative analysis of high-dimensional multi-omics data.
• Characterize gene function, pathway dependencies, and mechanisms of action through the design and analysis of perturbation studies and development of computational methods for functional genomics.
• Develop and apply computational frameworks for variant-to-gene-to-function inference, incorporating regulatory genomics and epigenomic data to link non-coding variation to gene function and disease biology.
• Translate computational analyses into clear, testable biological hypotheses and decision-support frameworks that inform target prioritization and therapeutic strategy.
• Collaborate closely with experimental scientists, computational biologists, and cross-functional teams to design studies, interpret results, and drive projects from data generation to biological insight.
• Drive digital innovation by identifying gaps in analytical workflows and developing robust, reproducible, and FAIR solutions that improve data interpretability, strengthen readout confidence, and accelerate therapeutic hypothesis generation.

Benefits

• A 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
• Flexible work models where possible.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees