Senior Scientist - Computational Discovery & Translational Analytics
Amgen
4 hours ago
Remote friendly (South San Francisco, CA)
United States
Clinical Research and Development
Senior Scientist - Computational Discovery & Translational Analytics
What You Will Do
- Develop and apply advanced computational methods to enable target discovery, validation, and biomarker identification across multimodal datasets (focus: surfaceome biology, perturbation screening, variant-to-gene-to-function).
- Integrate diverse biological data types (bulk, single-cell, spatial omics; perturbation datasets; genetic and epigenomic data) to generate insights into disease mechanisms, therapeutic hypotheses, and disease tissue context.
- Identify cell-type-specific and disease-relevant targets; characterize cellular context, heterogeneity, and activity in disease-relevant states via integrative high-dimensional multi-omics analysis.
- Characterize gene function, pathway dependencies, and mechanisms of action using perturbation studies and computational functional genomics methods.
- Develop and apply variant-to-gene-to-function inference frameworks using regulatory genomics and epigenomic data to link non-coding variation to gene function and disease biology.
- Translate analyses into clear, testable biological hypotheses and decision-support frameworks for target prioritization and therapeutic strategy.
- Collaborate with experimental scientists and cross-functional teams to design studies, interpret results, and drive projects from data generation to biological insight.
- Identify gaps in analytical workflows and develop robust, reproducible, FAIR solutions to improve interpretability, strengthen readout confidence, and accelerate hypothesis generation.
Basic Qualifications
- PhD in computational biology, bioinformatics, statistics, computer science, data science, or related quantitative discipline (and relevant post-doc where applicable)
- OR Master’s degree + 3 years of directly related experience
- OR Bachelor’s degree + 5 years of directly related experience
Preferred Qualifications
- Strong statistical modeling and AI/ML foundation applied to high-dimensional biological datasets.
- Experience with large-scale biological data (e.g., long-read RNA-seq, single-cell multi-omics, spatial omics, perturbation screens such as Perturb-seq, translatomics such as Ribo-seq, proteomics, and/or epigenomic data such as Hi-C/ATAC-seq).
- Proven ability to develop novel computational methods integrating complex multimodal datasets and translating results into testable hypotheses for target discovery/validation/biomarkers.
- Familiarity with disease tissue biology and interpreting data in biologically meaningful context.
- Strong programming skills in Python, R, or similar; experience with reproducible, well-documented workflows.
- Demonstrated record of innovative algorithm/model development via impactful publications, patents, or widely adopted tools.
- Excellent communication and collaboration across computational, experimental, and translational teams.
Benefits (as stated)
- Comprehensive benefits package including Retirement and Savings Plan, group medical/dental/vision, life/disability insurance, flexible spending accounts.
- Discretionary annual bonus (or sales incentive for field roles), stock-based long-term incentives, award-winning time-off, and flexible work models where possible.
Application Instructions
- Apply via careers.amgen.com.
What You Will Do
- Develop and apply advanced computational methods to enable target discovery, validation, and biomarker identification across multimodal datasets (focus: surfaceome biology, perturbation screening, variant-to-gene-to-function).
- Integrate diverse biological data types (bulk, single-cell, spatial omics; perturbation datasets; genetic and epigenomic data) to generate insights into disease mechanisms, therapeutic hypotheses, and disease tissue context.
- Identify cell-type-specific and disease-relevant targets; characterize cellular context, heterogeneity, and activity in disease-relevant states via integrative high-dimensional multi-omics analysis.
- Characterize gene function, pathway dependencies, and mechanisms of action using perturbation studies and computational functional genomics methods.
- Develop and apply variant-to-gene-to-function inference frameworks using regulatory genomics and epigenomic data to link non-coding variation to gene function and disease biology.
- Translate analyses into clear, testable biological hypotheses and decision-support frameworks for target prioritization and therapeutic strategy.
- Collaborate with experimental scientists and cross-functional teams to design studies, interpret results, and drive projects from data generation to biological insight.
- Identify gaps in analytical workflows and develop robust, reproducible, FAIR solutions to improve interpretability, strengthen readout confidence, and accelerate hypothesis generation.
Basic Qualifications
- PhD in computational biology, bioinformatics, statistics, computer science, data science, or related quantitative discipline (and relevant post-doc where applicable)
- OR Master’s degree + 3 years of directly related experience
- OR Bachelor’s degree + 5 years of directly related experience
Preferred Qualifications
- Strong statistical modeling and AI/ML foundation applied to high-dimensional biological datasets.
- Experience with large-scale biological data (e.g., long-read RNA-seq, single-cell multi-omics, spatial omics, perturbation screens such as Perturb-seq, translatomics such as Ribo-seq, proteomics, and/or epigenomic data such as Hi-C/ATAC-seq).
- Proven ability to develop novel computational methods integrating complex multimodal datasets and translating results into testable hypotheses for target discovery/validation/biomarkers.
- Familiarity with disease tissue biology and interpreting data in biologically meaningful context.
- Strong programming skills in Python, R, or similar; experience with reproducible, well-documented workflows.
- Demonstrated record of innovative algorithm/model development via impactful publications, patents, or widely adopted tools.
- Excellent communication and collaboration across computational, experimental, and translational teams.
Benefits (as stated)
- Comprehensive benefits package including Retirement and Savings Plan, group medical/dental/vision, life/disability insurance, flexible spending accounts.
- Discretionary annual bonus (or sales incentive for field roles), stock-based long-term incentives, award-winning time-off, and flexible work models where possible.
Application Instructions
- Apply via careers.amgen.com.