Principal Scientist, Translational Bioinformatics – Oncology

Bristol Myers Squibb; Needham (MA); On-site; USD 167,000 - 202,000; Full time

Benefits offered by this job

Comprehensive benefits

Flexible time off

11 paid holidays

Job summary

Bristol Myers Squibb invites applications for a Principal Scientist, Translational Computational Biology in Cambridge, MA. You will lead computational biology efforts embedded in oncology drug development, translating multimodal data into biomarker strategies and decision-grade recommendations.

You will collaborate across translational medicine, biology, and clinical development, advancing AI-enabled translational science, spatial biology, and reusable analytical workflows to benefit early-stage

Qualifications

• Ph.D. in computational biology or related field; 4+ years in academia/industry.
• Master’s with 6+ years experience in quantitative science.
• Bachelor’s with 8+ years experience in related field.

Responsibilities

• Lead translational computational biology efforts in oncology programs.
• Shape biomarker strategies and patient stratification approaches.
• Analyze multimodal molecular and clinical datasets across studies.
• Support target nomination, validation, and IND-enabling decisions.
• Translate analyses into clear, decision-grade narratives for program teams.

Skills

Programming in R/Python

Spatial biology

AI/ML translational problems

Causal inference

Cross-functional collaboration

Education

Ph.D. in computational biology or related field

Master’s degree with 6+ years

Bachelor’s degree with 8+ years

Tools

Nextflow/Snakemake

Git

Cloud platforms

Job description

Bristol Myers Squibb invites applications for a Principal Scientist, Translational Computational Biology in Cambridge, MA. You will lead computational biology efforts embedded in oncology drug development, translating multimodal data into biomarker strategies and decision-grade recommendations.

You will collaborate across translational medicine, biology, and clinical development, advancing AI-enabled translational science, spatial biology, and reusable analytical workflows to benefit early-stage