Associate Principal Scientist: Drug Discovery - Computational Biologist Multi-Omics
Listed on 2026-06-18
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Research/Development
Research Scientist, Biomedical Science, Biotechnology, Drug Discovery
Associate Principal Scientist:
Drug Discovery - Computational Biologist Multi-Omics
We are looking for a highly creative and motivated Computational Biologist for Multi-omics analysis to join Bayer's Chemical Biology, Imaging and Omics (CBIO) sub-cluster within Drug Discovery Sciences in the Bayer Research and Innovation Center (BRIC) in Cambridge, MA. In this role, you will be part of an interdisciplinary team of scientists supporting projects across the R&D portfolio and contributing to excellence in early drug discovery.
You will leverage your expertise in computational biology and data analysis skills to design and interpret Multi-omics experiments, addressing drug discovery challenges from target identification to drug development.
- Drive pivotal scientific questions in drug discovery projects, providing critical insights for go/no‑go decisions and advancing our understanding of targets and modalities.
- Design and execute integration of multi‑omics data (Chemo‑proteomics, transcriptomics), analyzing single or multiple omics layers to generate novel insights into disease, target, and compound biology.
- Develop and implement cutting‑edge computational tools and pipelines for complex multi‑omics data analysis.
- Actively participate in drug discovery project teams, contributing to target identification and validation, modality discovery, and mode‑of‑action deconvolution.
- Transform multi‑parametric data into testable hypotheses through iterative collaboration with wet‑lab scientists and data scientists.
- Champion AI‑enabled drug discovery and actively contribute to AI‑driven projects.
- Scout and evaluate innovative approaches through partnerships with CROs, biotech, and academia.
- Contribute to scientific publications and present at conferences to enhance our research visibility and demonstrate scientific excellence.
Required qualifications:
- Ph.D. in Computational Biology, Systems Biology, Chemical Biology, Biology, or related life sciences with demonstrated expertise in computational biology and a strong understanding of biology and experimental biology.
- Extensive hands‑on experience in multi‑omics data analysis (transcriptomics, proteomics, ATAC‑seq, Ribo‑seq, etc.). Application of these methods to the study of compound biology/chemical biology is a plus.
- Demonstrated success in designing and interpreting complex biological experiments, particularly in chemo‑omics.
- Proven ability to extract meaningful biological insights from quantitative datasets through deep understanding of chemical biology, cell biology, and disease biology.
- Track record of successfully integrating multiple omics datasets to generate novel insights.
- Strong understanding of experimental workflows and ability to collaborate with wet‑lab colleagues for optimization and troubleshooting.
- Expert knowledge of programming languages (Python or R) and statistical methods relevant to computational biology.
- Experience in chemo‑informatics or chemo‑genomics is highly desirable.
- Knowledge of spatial omics analysis is advantageous.
- Outstanding interpersonal and communication skills with demonstrated ability to work effectively in cross‑disciplinary teams.
Preferred qualifications:
- 4+ years of postdoctoral experience or relevant industry experience.
Salary range: $ – $. Additional compensation may include a bonus or commission where applicable. Benefits include health care, vision, dental, retirement, PTO, sick leave, and more.
LocationUnited States:
Massachusetts:
Cambridge
Pharmaceuticals
Reference Code870320
Equal Opportunity EmployerBayer is an Equal Opportunity Employer, including Disabled/Veterans. Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodations. Bayer is an E‑Verify Employer.
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