Senior Manager

About the Role

This is a new position within the cutting-edge Drug Development Data Science and Advanced Analytics (DSAA) team, focused on advancing the global drug development process. The role requires strong computational, statistical, and biological capabilities, with a demonstrated track record of translating complex, multi-modal data into testable hypotheses and actionable insights. You will integrate deep expertise in digital health data science—including wearable and sensor-derived longitudinal data—with broader contributions across genomics, proteomics, imaging, flow cytometry, and other biomarker data types from clinical trials. As a hands-on individual contributor, you will drive exploratory analysis (both hypothesis-generating and hypothesis-driven) for scientific questions related to drug development and clinical study design. Responsibilities include defining approaches, processes, algorithms, and pipelines to support analytics, visualization, and decision support for drug development scientists and project teams, while collaborating closely with Biostatistics leads and cross-functional partners.

Responsibilities

Digital Health & Wearable Data Science (Deep Expertise)

• 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).

Broader Multi-Modal Data Science (Clinical Trial & Drug Development)

• 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.

Collaboration & Technical Contribution

• 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.

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.

Preferred Qualifications

• Experience with genomics, proteomics, imaging, flow cytometry, or immunobiology datasets from clinical trials is highly preferred.
• Experience with NLP is highly preferred.
• Experience with Survival Analysis and time-to-event modeling is highly preferred.
• Experience with causal ML and explainable AI is highly preferred.
• Knowledge of molecular biology and understanding of disease pathways is preferred.
• 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.

Why Join Us

• Work at the intersection of digital biomarkers, multi-omics, imaging, machine learning, and clinical drug development.
• Partner with multidisciplinary teams to advance analytics from real-world and trial-based wearable signals alongside genomics, proteomics, and other rich clinical trial data.
• Build practical, validated solutions with high impact across early-to-late phase clinical development, and write the code that delivers them.
• Play a key role in shaping the future of data-driven drug development at Bristol-Myers Squibb.

Pay

Princeton - NJ - US: $164,110 - $198,862. The starting compensation range(s) for this role are listed for a full-time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. The starting pay rate takes into account characteristics of the job, such as required skills, where the job is performed, the employee’s work schedule, job-related knowledge, and experience. Final, individual compensation will be decided based on demonstrated experience.

Benefits

Benefit offerings are subject to the terms and conditions of the applicable plans in effect at the time and may require enrollment. Our benefits include:

• Health Coverage: Medical, pharmacy, dental, and vision care.
• Wellbeing Support: Programs such as BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
• Financial Well-being and Protection: 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, and survivor support.
• Work-life Benefits Include:
  • Paid Time Off:
    • US Exempt Employees: flexible time off (unlimited, with manager approval), 11 paid national holidays (not applicable to employees in Phoenix, AZ, Puerto Rico, or Rayzebio employees).
    • Phoenix, AZ, Puerto Rico, and Rayzebio Exempt, Non-Exempt, Hourly Employees: 160 hours annual paid vacation for new hires with manager approval, 11 national holidays, and 3 optional holidays.
  • Based on eligibility, additional time off for employees may include unlimited paid sick time, up to 2 paid volunteer days per year, summer hours flexibility, leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs, and an annual Global Shutdown between Christmas and New Year’s Day. All global employees full and part-time who are actively employed at and paid directly by BMS at the end of the calendar year are eligible to take advantage of the Global Shutdown.