Principal Scientist, Computational Biology
Bristol Myers Squibb
August 13, 2026
Remote friendly (Cambridge, MA)
United States
Clinical Research and Development
Responsibilities:
- Perform computational research and integrative analyses on multimodal biological profiling datasets (transcriptomic, proteomics, single-cell omics, spatial profiling) to support early pipeline programs.
- Apply machine learning and advanced computational/statistical approaches to compare high-dimensional perturbation readouts (CRISPR screens, perturb-seq, cellular imaging) to patient-defined disease states; nominate and validate new targets for Neuro, Cardiovascular, and Autoimmune indications.
- Collaborate with external partners (industry, academia, pre-competitive collaborations such as NIH AMP) on novel computational and experimental approaches.
- Communicate findings and recommend follow-up actions (1:1s, seminars, team meetings).
Basic Qualifications:
- PhD in a quantitative field (computational biology, computational genomics/genetics, computer science, statistics, mathematics, or related) and 5+ years post-graduate experience.
- Advanced hands-on knowledge of R or Python for computational and reproducible research.
Preferred Qualifications:
- 5+ years post-graduate computational biology research experience (biopharma preferred) with publications/track record.
- Experience integrating high-dimensional multi-omics, single-cell (CITE-seq, scATAC-seq, perturb-seq), and/or spatial profiling.
- Strong statistical and ML skills (e.g., single-cell foundation models).
- Human disease biology background, especially autoimmune diseases.
- Experience with computer vision for cellular imaging; AI agent tools (e.g., Claude coding); strong self-learning and oral/written communication.
Location: Cambridge, MA (no fully remote option).
Compensation Overview: $166,770–$202,086.
Application instruction: Apply even if you don’t perfectly match your resume.
- Perform computational research and integrative analyses on multimodal biological profiling datasets (transcriptomic, proteomics, single-cell omics, spatial profiling) to support early pipeline programs.
- Apply machine learning and advanced computational/statistical approaches to compare high-dimensional perturbation readouts (CRISPR screens, perturb-seq, cellular imaging) to patient-defined disease states; nominate and validate new targets for Neuro, Cardiovascular, and Autoimmune indications.
- Collaborate with external partners (industry, academia, pre-competitive collaborations such as NIH AMP) on novel computational and experimental approaches.
- Communicate findings and recommend follow-up actions (1:1s, seminars, team meetings).
Basic Qualifications:
- PhD in a quantitative field (computational biology, computational genomics/genetics, computer science, statistics, mathematics, or related) and 5+ years post-graduate experience.
- Advanced hands-on knowledge of R or Python for computational and reproducible research.
Preferred Qualifications:
- 5+ years post-graduate computational biology research experience (biopharma preferred) with publications/track record.
- Experience integrating high-dimensional multi-omics, single-cell (CITE-seq, scATAC-seq, perturb-seq), and/or spatial profiling.
- Strong statistical and ML skills (e.g., single-cell foundation models).
- Human disease biology background, especially autoimmune diseases.
- Experience with computer vision for cellular imaging; AI agent tools (e.g., Claude coding); strong self-learning and oral/written communication.
Location: Cambridge, MA (no fully remote option).
Compensation Overview: $166,770–$202,086.
Application instruction: Apply even if you don’t perfectly match your resume.