Career Category
Information Systems
Job Description
What you will do
Let’s do this. Let’s change the world.
We are seeking a highly qualified and motivated Principal Data Scientist with deep expertise 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, applying advanced digital technologies to accelerate the path from target inception through drug development. Within ARIA, Bioinformatics Technologies develops and deploys innovative computational methods that enhance target identification, mechanistic characterization, early validation, and therapeutic discovery.
In this role, you will lead and expand computational and systems biology capabilities that support Amgen’s pre-clinical discovery engine, with a strong emphasis on generating insights from functional genomics, multimodal omics, and other high-dimensional biological datasets. You will provide both scientific and people leadership to a team of data scientists applying next-generation computational, statistical, and AI/ML approaches to identify therapeutic targets, uncover biomarkers, refine disease hypotheses, and guide experimental strategies across early research. This position plays a critical role in strengthening the data-driven foundation of Amgen’s pre-clinical value chain by elevating analytical rigor and improving interpretation of complex biological data.
The ideal candidate brings strong analytical aptitude, deep technical expertise in computational sciences, and a solid theoretical foundation in advanced statistical and machine learning methods. These skills must be complemented by a strong understanding of molecular biology and functional genomics, along with a proven track record of innovative and collaborative research demonstrated through impactful peer-reviewed publications.
RESPONSIBILITIES:
- Mentor junior biological data scientists working on functional genomics, disease biology, and multi-omics analytics to support pre-clinical target discovery and early validation.
- Develop, implement, and apply advanced computational / AI/ML / statistical methods and workflows to analyze high-dimensional multi-modal omics datasets for target identification, mechanism elucidation, and biomarker hypothesis generation.
- Drive innovation in design and execution of computational analyses supporting high-throughput screens to map gene-phenotype relationships, prioritize candidate targets, and guide experimental follow-up.
- Collaborate with experimental biology and translational research teams to align computational predictions with biological context, guide study design, and translate computational insights into actionable pre-clinical hypotheses.
- Design and implement computational workflows for cutting-edge functional genomics and multi-omics technologies (e.g., single-cell, spatial, long-read sequencing, phenotypic and virtual perturbation screens), using advanced AI/ML/deep-learning methods to simulate cellular responses, derive mechanistic insights, and prioritize therapeutic hypotheses for preclinical validation.
- Foster a culture of scientific innovation and collaboration within the team by encouraging creative problem-solving, continuous learning, cross-disciplinary exchange, and reproducible experimentation with emerging computational and omics methodologies.
- Communicate project results and data-driven recommendations to stakeholders and cross-functional partners.
- Monitor, review, and critically interpret published computational research in the field of computational biology, bioinformatics, and systems biology.
Win
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The dynamic professional we seek is a scientist with these qualifications.
Basic Qualifications:
Doctorate degree in computer science, biomedical data science, computational biology, bioinformatics, or systems biology; preferably with post-doctoral training and industry experience
Or
Master’s degree and 6+ years of biomedical data science, computational biology, bioinformatics, systems biology or related field
Or
Bachelor’s degree and 8+ years of biomedical data science, computational biology, bioinformatics, systems biology, or related field
Preferred Qualifications:
- Demonstrated track record of scientific productivity in computational biology, bioinformatics, or related research settings.
- Track record of mentoring, team leadership or supervision within a formal or matrix structure, preferably in a multidisciplinary environment, helping to grow junior scientists and foster innovation, collaboration, and scientific rigor within a data-science team.
- Proven experience working at the intersection of computational biology, functional genomics, and drug-discovery/preclinical research (e.g., target identification, biomarker discovery, mechanistic modeling).
- Demonstrated success in applying machine learning, deep learning, generative modeling, topological data analysis, or causal inference to multimodal omics, functional genomics, single-cell, spatial, or long-read sequencing data to generate biologically meaningful hypotheses.
- Strong programming and data-science skills, including proficiency in Python and/or R; demonstrated experience with large-scale data analysis and high-dimensional biological data; and comfort working in high-performance or cloud computing environments under modern data-science workflows.
- Familiarity with molecular and disease biology, with the ability to contextualize computational findings in therapeutic discovery.
- Excellent analytical and communication skills, with the ability to extract and clearly present insights from complex data to diverse audiences with rigor and accuracy.
- Strong interpersonal and collaborative skills with demonstrated ability to thrive in cross-functional teams and effectively present results to diverse audiences.
- 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.
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