Senior Manager

Bristol Myers Squibb•Princeton, NJ

About The Position

This is a new position. You will join a cutting-edge Drug Development Data Science and Advanced Analytics (DSAA) team to advance the global drug development process. We are looking for a candidate with strong computational, statistical, and biological capabilities and a demonstrated track record of translating complex, multi-modal data into testable hypotheses and actionable insights. This role brings together 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 generated 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. You will define approaches, processes, algorithms, and pipelines that support analytics, visualization, and decision support needs of drug development scientists and project teams, while collaborating closely with Biostatistics leads and cross-functional partners across the organization. We are looking for 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 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

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

Benefits

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 Years 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.
Eligibility Disclosure: The summer hours program is for United States (U.S.) office-based employees due to the unique nature of their work. Summer hours are generally not available for field sales and manufacturing operations and may also be limited for the capability centers. Employees in remote-by-design or lab-based roles may be eligible for summer hours, depending on the nature of their work, and should discuss eligibility with their manager. Employees covered under a collective bargaining agreement should consult that document to determine if they are eligible. Contractors, leased workers and other service providers are not eligible to participate in the program.

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