Principal Scientist Computational Biology
Listed on 2025-12-14
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Research/Development
Research Scientist, Data Scientist, Biomedical Science
Working with Us
Challenging. Meaningful. Life-changing. Those aren't words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it.
You'll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible.
Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: .
Job DescriptionWe seek a creative and passionate computational scientist to join the Neuroscience, Immunology, and Cardiovascular (NIC) discovery team within Informatics and Predictive Sciences, a globally distributed group driving innovative computational research for discovery and early development. In this role, you will apply your analytical skills to single cell, multi-omics, spatial profiling as well as gene perturbation datasets generated from both patients and model organisms.
You will work as part of a cross‑functional team focused on Neuroscience, Immunology and Cardiovascular early pipeline programs. In so doing, you will contribute to the discovery of targets and compounds that directly address unmet medical need in patients within NIC space especially those with neurodegenerative diseases.
Location:
Cambridge, MA
- Perform computational research on high-dimensional readouts from perturbation experiments/screens (e.g. CRISPR screens, perturb-seq, cellular imaging)
- Apply machine learning, and other advanced computational approaches, to compare high-dimensional experimental readouts to disease states defined by patient data (including transcriptomics, proteomics, single cell omics, and imaging)
- Work as part of a cross‑functional team that will nominate and validate new targets for neurodegeneration and neuropsychiatry
- Work with external partners in industry, academia, and pre‑competitive collaborations (e.g. NIH Accelerating Medicines Partnership) on novel computational and experimental approaches
- Communicate findings and recommend follow‑up actions in multiple settings (including 1:1, seminars, and team meetings)
- Bachelor's Degree 8+ years of academic / industry experience
- Or Master's Degree 6+ years of academic / industry experience
- Or PhD 4+ years of academic / industry experience
- PhD from a recognized institution in a quantitative field such as computational biology, computational genomics/genetics, computer science, statistics, mathematics, or other related discipline and 5+ years of post‑graduate experience
- Advanced hands‑on knowledge of at least one high-level programming language such as R or Python for computational research and reproducible research practices
- 5+ years of post‑graduate experience in computational biology research (biopharma industry preferred) with track record (such as scientific publications) in driving and advancing research projects/programs with computational approaches
- Hands on experience analyzing and integrating high-dimensional molecular datasets such as multi‑omics (RNA‑seq, ATAC‑seq, proteomics, ChIP‑seq/CUT&RUN ), single cell (CITE‑seq, scATAC‑seq, perturb‑seq) and spatial profiling (Visium, Geo Mx, CosMX )
- Experience implementing and/or developing statistical methodologies and machine learning algorithms applied to the biological problems
- Background in Neuroscience, especially in neuro‑degenerative diseases strongly preferred
- Experience applying computer vision models to cellular imaging data preferred
- Scientific curiosity with an ability of self‑learning
- Strong oral and written communication skills
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