Senior Scientist, Biologics Discovery, Discovery Biotherapeutics

Bristol Myers Squibb•Brisbane, CA

1d•Onsite

About The Position

The Biologics Discovery organization at Bristol Myers Squibb advances next-generation biotherapeutics through innovative experimental platforms and data-enabled discovery. We integrate immune-based discovery, display technologies, and computational analysis to support high-quality decision-making across early research programs. We are seeking a Senior Scientist to join our team in Brisbane, CA. This role is a primarily lab-based scientific position that will contribute directly to biologics discovery by driving immune-based antibody discovery using advanced single-cell technologies, while also enabling expansion into cyclic-peptide discovery via phage display. The position requires strong experimental ownership, coupled with the ability to apply data-driven and computational approaches in close collaboration with computational biology and machine learning partners. The role offers an opportunity to make a meaningful scientific impact by helping evolve discovery platforms that integrate experimental execution with modern analytical and computational insights.

Requirements

PhD in Immunology, Molecular Biology, Biochemistry, Structural Biology, Bioengineering, or a related scientific discipline with 2+ years of industry experience; or an MS with 5+ years of industry experience; or BS with 7+ years of industry experience.

Nice To Haves

Hands-on experience in therapeutic antibody discovery, including immune-based workflows.
Proficiency in phage display–based discovery, including biopanning, selection, and NGS, preferably using cyclic-peptide libraries; antibody or mRNA phage library experience will also be considered.
Demonstrated ability to independently design, execute, and interpret experiments under general scientific direction.
Strong background in molecular biology, flow cytometry, and assay-based screening.
Experience analyzing and integrating complex experimental datasets to inform scientific decision-making.
In-depth understanding of antibody sequence analysis, structure–function relationships, and developability assessment is strongly preferred, including experience with PyMOL, AlphaFold, MOE, or related tools.
Experience applying data visualization and analytical tools (e.g., Spotfire, R, Python, Geneious).
Familiarity with bioinformatics pipelines and associated concepts used to analyze NGS data.
Experience working in integrated or high-throughput discovery environments.
Prior experience collaborating with machine learning or generative discovery teams, preferably in closed-loop or iterative discovery systems.
Strong interpersonal skills and ability to work independently while influencing cross-functional discovery biotherapeutics, biology, automation, and engineering teams.
Excellent written and verbal communication skills.

Responsibilities

Design and execute immune-based antibody discovery workflows, including immunization schemas design (mRNA and protein), immune cell processing, and application of flow cytometry-based techniques for immune cell analysis, enrichment, and validation.
Lead single-cell antibody discovery efforts (e.g., 10x Genomics), including sample preparation, library generation, and data interpretation.
Apply understanding of antibody sequence, structure, and developability, with preference for experience using PyMOL, AlphaFold, MOE or related tools for antibody structure prediction and visualization, to guide hit selection and progression.
Design and execute phage display campaigns to support cyclic-peptide discovery, including protein- and cell-based panning strategies, and progress cyclic-peptide hits from phage selections into downstream characterization.
Analyze and integrate large, complex, and high-dimensional datasets generated across antibody and cyclic-peptide discovery workflows.
Apply data-driven and computational approaches (e.g., sequence and enrichment analysis; visualization tools such as Spotfire; scripting or statistical tools such as R or Python as a plus) to inform experimental design and hit triage.
Work closely with computational biology, machine learning, and generative discovery colleagues, with preferred experience contributing to closed-loop discovery systems that iteratively connect data generation, analysis, and experimental decision-making.
Maintain high-quality experimental documentation and contribute to data-driven discovery discussions.

Benefits

Medical, pharmacy, dental, and vision care.
Programs such as BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
401(k) plan.
Short- and long-term disability.
Life insurance.
Accident insurance.
Supplemental health insurance.
Business travel protection.
Personal liability protection.
Identity theft benefit.
Legal support.
Survivor support.
Flexible time off (unlimited, with manager approval, for US Exempt Employees).
11 paid national holidays (not applicable to employees in Phoenix, AZ, Puerto Rico or Rayzebio employees).
160 hours annual paid vacation for new hires with manager approval (for Phoenix, AZ, Puerto Rico and Rayzebio Exempt, Non-Exempt, Hourly Employees).
3 optional holidays (for Phoenix, AZ, Puerto Rico and Rayzebio Exempt, Non-Exempt, Hourly Employees).
Unlimited paid sick time (based on eligibility).
Up to 2 paid volunteer days per year (based on eligibility).
Summer hours flexibility (based on eligibility).
Leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs.
Annual Global Shutdown between Christmas and New Years Day.