Senior Scientist - Computational Systems & Predictive Biology
Amgen
5 hours ago
Remote friendly (South San Francisco, CA)
United States
Clinical Research and Development
Senior Scientist - Computational Systems & Predictive Biology
What You Will Do
- Develop scientific platforms and predictive systems for scalable, reproducible, therapeutic-area-agnostic computational biology across target discovery and validation.
- Develop and operationalize computational models into reusable systems; define canonical data and analytical representations across modalities; build predictive frameworks for in silico interrogation of biological systems where experimental data are limited or infeasible.
Key Responsibilities
- Develop and implement computational systems that standardize and operationalize data, models, and analytical methods into reusable, scalable frameworks for target discovery, validation, and prioritization.
- Define and build canonical data representations and analytical abstractions across multimodal datasets (e.g., perturbation biology, surfaceome features, variant-to-gene-to-function) for consistent TA-agnostic application.
- Design and develop predictive systems to model molecular and cellular profiles for in silico interrogation when experiments are limited/infeasible.
- Translate computational models into user-facing tools, platforms, interfaces, internal applications, and agentic systems to improve accessibility and impact.
- Enable experimental scientists by accelerating data analysis, guiding experimental design, and generating actionable hypotheses for modality-driven target discovery/validation.
- Collaborate with computational and experimental scientists on system design; partner with data engineering/technology teams for deployment and scaling while maintaining ownership of modeling, data abstractions, and analytical design.
- Drive innovation by identifying gaps in computational workflows and integrating emerging approaches (including generative modeling and representation learning).
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 directly related experience
OR
- Bachelor’s degree + 5 years directly related experience
Preferred Qualifications / Skills
- Strong computational biology methods background, including statistical modeling and AI/ML; ability to model biological systems.
- Expertise in machine learning for biological data; predictive modeling and ideally generative/representation learning.
- Experience with multimodal biological datasets (perturbation screens, transcriptomics, single-cell & spatial omics, proteomics, genetic/epigenomic data) and integrating them into unified frameworks.
- Ability to develop computational abstractions/canonical representations for consistent, reusable analysis across datasets/modalities/disease contexts.
- Experience building predictive or generative models to infer molecular/cellular responses when experimental data are limited/incomplete.
- Proven ability to translate computational models into scalable, reusable tools/frameworks adopted by scientists.
- Strong programming skills in Python, R, Linux/Unix (or similar); modular, maintainable codebases.
- Familiarity with workflow management systems (e.g., Nextflow) and AWS cloud infrastructure (plus).
- Cross-functional experience; strong communication/collaboration; translate complex computational results into biologically meaningful insights.
- Track record of computational innovation (publications, patents, or contributions to methods/frameworks/scientific software).
Benefits (as explicitly stated)
- Total Rewards Plan (eligibility-based), including employee benefits: Retirement and Savings Plan, medical/dental/vision, life/disability insurance, flexible spending accounts; discretionary annual bonus; stock-based long-term incentives; award-winning time-off plans; flexible work models where possible.
Application Instructions
- Apply via careers.amgen.com.
- No application deadline; applications continue until a sufficient number is received or a candidate is selected.
- Sponsorship not guaranteed.
What You Will Do
- Develop scientific platforms and predictive systems for scalable, reproducible, therapeutic-area-agnostic computational biology across target discovery and validation.
- Develop and operationalize computational models into reusable systems; define canonical data and analytical representations across modalities; build predictive frameworks for in silico interrogation of biological systems where experimental data are limited or infeasible.
Key Responsibilities
- Develop and implement computational systems that standardize and operationalize data, models, and analytical methods into reusable, scalable frameworks for target discovery, validation, and prioritization.
- Define and build canonical data representations and analytical abstractions across multimodal datasets (e.g., perturbation biology, surfaceome features, variant-to-gene-to-function) for consistent TA-agnostic application.
- Design and develop predictive systems to model molecular and cellular profiles for in silico interrogation when experiments are limited/infeasible.
- Translate computational models into user-facing tools, platforms, interfaces, internal applications, and agentic systems to improve accessibility and impact.
- Enable experimental scientists by accelerating data analysis, guiding experimental design, and generating actionable hypotheses for modality-driven target discovery/validation.
- Collaborate with computational and experimental scientists on system design; partner with data engineering/technology teams for deployment and scaling while maintaining ownership of modeling, data abstractions, and analytical design.
- Drive innovation by identifying gaps in computational workflows and integrating emerging approaches (including generative modeling and representation learning).
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 directly related experience
OR
- Bachelor’s degree + 5 years directly related experience
Preferred Qualifications / Skills
- Strong computational biology methods background, including statistical modeling and AI/ML; ability to model biological systems.
- Expertise in machine learning for biological data; predictive modeling and ideally generative/representation learning.
- Experience with multimodal biological datasets (perturbation screens, transcriptomics, single-cell & spatial omics, proteomics, genetic/epigenomic data) and integrating them into unified frameworks.
- Ability to develop computational abstractions/canonical representations for consistent, reusable analysis across datasets/modalities/disease contexts.
- Experience building predictive or generative models to infer molecular/cellular responses when experimental data are limited/incomplete.
- Proven ability to translate computational models into scalable, reusable tools/frameworks adopted by scientists.
- Strong programming skills in Python, R, Linux/Unix (or similar); modular, maintainable codebases.
- Familiarity with workflow management systems (e.g., Nextflow) and AWS cloud infrastructure (plus).
- Cross-functional experience; strong communication/collaboration; translate complex computational results into biologically meaningful insights.
- Track record of computational innovation (publications, patents, or contributions to methods/frameworks/scientific software).
Benefits (as explicitly stated)
- Total Rewards Plan (eligibility-based), including employee benefits: Retirement and Savings Plan, medical/dental/vision, life/disability insurance, flexible spending accounts; discretionary annual bonus; stock-based long-term incentives; award-winning time-off plans; flexible work models where possible.
Application Instructions
- Apply via careers.amgen.com.
- No application deadline; applications continue until a sufficient number is received or a candidate is selected.
- Sponsorship not guaranteed.