Senior Manager

Bristol Myers Squibb•Princeton, NJ

About The Position

This is a new position within a cutting-edge Drug Development Data Science and Advanced Analytics (DSAA) team at Bristol Myers Squibb, aimed at advancing the global drug development process. The role seeks a candidate with strong computational, statistical, and biological capabilities, and a proven ability to translate complex, multi-modal data into testable hypotheses and actionable insights. This position specifically requires deep expertise in digital health data science, including wearable and sensor-derived longitudinal data, while also contributing across genomics, proteomics, imaging, flow cytometry, and other biomarker data types from clinical trials. As a hands-on individual contributor, the Senior Manager will lead exploratory analysis (both hypothesis-generating and hypothesis-driven) for scientific questions related to drug development and clinical study design. The role involves defining approaches, processes, algorithms, and pipelines to support analytics, visualization, and decision support for drug development scientists and project teams, in close collaboration with Biostatistics leads and cross-functional partners. The ideal candidate is a hands-on, state-of-the-art practitioner.

Requirements

Ph.D. in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, Computer Science, or related field) and 1+ years of academic/industry experience; or Master's Degree in a relevant quantitative field and 3+ years of industry experience
Deep, hands-on expertise in digital health data science, including wearable/sensor time-series data (QC, preprocessing, artifact handling, imputation, feature engineering for accelerometry/actigraphy, HRV, SpO₂)
Strong Python skills with evidence of shipping production-quality code: clean, testable, object-oriented design; modular pipelines; Git/version control; and collaborative development practices
Strong experience in biomarker or multi-modal data analysis with data generated from clinical trials or electronic health records
Experience in modeling methods particularly in their application to pharma R&D; experience in the application of AI/ML; proficiency in Python, R, SQL, and cloud platforms
Experience developing statistical and machine learning models on high-dimensional data for time-to-event and longitudinal outcomes
Familiarity with clinical trial design, drug development processes, and the role of biomarkers in regulatory and clinical decision-making
Perspective in leveraging innovative approaches to expedite drug development and address the complexities of emerging data
Ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects
Strong problem-solving and collaboration skills, and rigorous and creative thinking
Excellent communication, data presentation, and visualization skills
Capable of establishing strong working relationships across the organization

Nice To Haves

Experience with genomics, proteomics, imaging, flow cytometry, or immunobiology datasets from clinical trials
Experience with NLP
Experience with Survival Analysis and time-to-event modeling
Experience with causal ML and explainable AI
Knowledge of molecular biology and understanding of disease pathways
Familiarity with sleep analytics, circadian cosinor modeling, or biomechanical/navigational physics for movement data (quaternions, Euler angles, orientation estimation)
Experience managing or integrating third-party analytics and validating vendor outputs
Experience with scalable compute and deployment patterns, including AWS multi-GPU instances and parallelization for model training/inference

Responsibilities

Build and maintain Python pipelines for wearable and sensor-derived time-series data, including QC, preprocessing, sensor artifact removal, imputation, and feature engineering based on clinical concepts of interest
Develop and validate models for longitudinal sensor data using frequency/time-frequency representations, digital filtering, representation learning, and deep learning approaches (e.g., Transformers, ensembles) with model explainability techniques where appropriate
Apply statistically rigorous approaches to repeated-measures and longitudinal data, including mixed-effects/hierarchical models and study-appropriate strategies for within-subject dynamics and missingness
Drive quantitative characterization of physiological and clinically meaningful measures (e.g., accelerometry/actigraphy, HRV, SpO₂) associated with disease progression or patient subtyping
Collaborate with and perform QC/validation of third-party analytics providers and vendor-derived digital biomarker outputs
Implement strong evaluation practices and reproducible research standards (nested CV, LOO, OOB methods, structured codebases, version control)
Develop and apply novel or existing computational methods for patient segmentation and biomarker discovery from multimodal clinical, digital health, and omics datasets in partnership with Translational, Clinical, and Statistical Scientists
Execute data science and biomarker analyses on datasets from BMS clinical trials and real-world data cohorts, spanning genomics, proteomics, imaging, flow cytometry, and other high-dimensional biomarker data types
Partner with lead and protocol statisticians in contributing to statistical analysis plans (SAPs) for exploratory biomarker and digital health analyses, highlighting the data science strategy for clinical drug development
Perform relevant and innovative statistical analyses of high-dimensional data (e.g., gene expression, sequencing, imaging features) generated by cutting-edge technologies
Develop novel ways of integrating, mining, and visualizing diverse, high-dimensional, and disparate datasets across early-to-late phase drug development
Formulate, implement, test, and validate predictive models and implement efficient automated processes for producing modeling results at scale
Leverage modern machine learning capabilities, including AI/ML, deep learning, NLP, causal ML, and explainable AI, across multiple data modalities and clinical development contexts
Contribute to the scientific and statistical strategy of drug development, including development of predictive biomarkers and precision medicine approaches
Collaborate with cross-functional teams, including clinicians, data scientists, translational medicine scientists, biostatisticians, and IT/engineering professionals
Contribute to team excellence via code reviews, technical mentorship, and raising the overall engineering and methodological rigor of the team
Communicate analytical results clearly and effectively to both technical and non-technical stakeholders, with strong data presentation and visualization skills
Manage and coordinate resources to produce quality deliverables within timelines for competing priorities
Build and maintain strong working relationships across the organization

Benefits

Medical, pharmacy, dental, and vision care
BMS Well-Being Account
BMS Living Life Better
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 (Phoenix, AZ, Puerto Rico and Rayzebio Exempt, Non-Exempt, Hourly Employees)
3 optional holidays (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