Principal Scientist, Neuroscience, Immunology, and CV (NIC) Translational Informatics and Predictive Sciences

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: careers.bms.com/working-with-us.

We seek a creative and passionate computational biologist with a strong background in clinical data analysis to join the Neuroscience, Immunology, and Cardiovascular (NIC) Translational Informatics team within Informatics and Predictive Sciences (IPS). In this role, you will use machine learning and advanced statistical approaches to analyze high-dimensional data from late-stage clinical trials and longitudinal patient cohort profiling.

In support of an exciting late-stage neuroscience portfolio, your responsibilities will include patient subgroup stratification, biomarker discovery for treatment response and resistance, and mechanistic analyses to support Life Cycle Management (LCM) and indication expansion. You will also contribute to reverse translation, leveraging clinical insights to inform early research and drive bedside-to-bench innovation beyond the current portfolio. Your work will directly impact the development of novel therapies for patients with neurodegenerative and neuropsychiatric diseases.

Key Responsibilities

• Analyze high-dimensional datasets from late-stage clinical trials and multi-modal real-world datasets from commercial partners, pre-competitive consortiums, and public resources.
• Work with diverse data modalities, including clinical data (ADaM/SDTM), high throughput proteomics (e.g., Olink), Whole Genome Sequencing, genotyping array, RNA-Seq and multiplex flow cytometry.
• Perform deep and innovative analyses of these data to build comprehensive understanding of disease mechanisms and patient heterogeneity, identify non-responders to standards of care with unmet medical needs, generate testable hypotheses for drug differentiation, combination strategies, and novel indication opportunities.
• Optimize reverse translation through application of late-stage clinical data sets to inform early-stage clinical trials and the discovery pipeline, in close collaboration with teams across Research and Development, including bioinformaticians, statisticians, biologists, biomarker leads, and clinicians.
• Communicate findings and recommend follow-up actions in multiple settings (including 1:1, seminars, and team meetings). Participate in authorship of scientific reports and present methods and conclusions to publishable standards.

Basic Qualifications

• Bachelor’s degree with 8+ years of academic/industry experience, or
• Master’s degree with 6+ years of experience, or
• PhD with 4+ years of experience in computational biology, computational neuroscience, or a related field

Preferred Qualifications

• PhD from a recognized institution in a quantitative field such as computational biology, computational neuroscience or related
• Experience analyzing clinical data from interventional clinical trials, including integration of structured clinical endpoints (e.g., ADaM/SDTM), biomarker data (e.g., lab measures), and longitudinal assessments to support pharmacodynamic analyses, as well as the identification of predictive, prognostic, or surrogate endpoint biomarkers.
• Experience analyzing and integrating high-dimensional molecular datasets such as multi-omics (e.g., RNA-seq), single cell (scRNA-seq), genome-wide genotyping array, or spatial transcriptomics datasets
• Advanced hands-on knowledge of at least one high-level programming language such as R or Python for computational and reproducible research practices
• Track record (such as scientific publications) in driving and advanc