Principal Scientist, Computational Biology

About the role

Bristol Myers Squibb seeks a creative and passionate computational biologist to join the Neuroscience, Immunology, and Cardiovascular (NIC) discovery team within Informatics and Predictive Sciences. This role involves performing computational research and integrative analyses on multimodal biological profiling datasets, applying machine learning and other advanced computational/statistical approaches, and contributing to the discovery of targets and compounds for Neuro, Cardiovascular, and Autoimmune diseases.

Responsibilities

• Perform computational research and integrative analyses on multimodal biological profiling datasets (e.g. transcriptomic, proteomics, single cell omics, spatial profiling) that you help to design together with wet lab scientists to support early pipeline programs.
• Apply machine learning, and other advanced computational/statistical approaches, to compare high-dimensional experimental readouts from perturbation experiments/screens (e.g. CRISPR screens, perturb-seq, cellular imaging) to disease states defined by patient data and work as part of a cross-functional team to nominate and validate new targets for Neuro, Cardiovascular and Autoimmune diseases indications.
• 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).

Requirements

• 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.
• Strong quantitative skills, with hand-on experience implementing and/or developing statistical methodologies and machine learning algorithms (such as single cell foundation models) applied to the biological problems.
• Background in human disease biology, especially in areas of autoimmune diseases strongly preferred.
• Experience applying computer vision models to cellular imaging data preferred.
• Experience utilizing and applying AI agents such as Claude coding to accelerate workflow preferred.
• Scientific curiosity with an ability of self-learning.
• Strong oral and written communication skills.

Qualifications

• Basic Qualifications: 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.
• Preferred Qualifications: 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 profiling (CITE-seq, scATAC-seq, perturb-seq) and/or spatial profiling to derive novel biological insights; Strong quantitative skills, with hand-on experience implementing and/or developing statistical methodologies and machine learning algorithms (such as single cell foundation models) applied to the biological problems; Background in human disease biology, especially in areas of autoimmune diseases strongly preferred; Experience applying computer vision models to cellular imaging data preferred; Experience utilizing and applying AI agents such as claude coding to accelerate workflow preferred; Scientific curiosity with an ability of self-learning; Strong oral and written communication skills.