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About the Role

Bristol Myers Squibb is seeking a Senior Manager, Data Science to join their Drug Development Data Science & Advanced Analytics (DSAA) team. This individual contributor role focuses on applying computational science and statistical rigor to drug development, analyzing complex multi-modal data to drive clinical decisions and advance the global development pipeline.

Responsibilities

Develop and apply novel computational methods for patient segmentation, biomarker discovery, and hypothesis generation from multimodal clinical and omics datasets.
Execute data science 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.
Develop innovative approaches to integrating, mining, and visualising diverse, high-dimensional, and disparate datasets generated across early-to-late phase drug development.
Formulate, implement, test, and validate predictive models and build efficient, automated processes for delivering modelling results at scale.
Apply modern machine learning capabilities — including AI/ML, deep learning, NLP, causal ML, and explainable AI — across multiple data modalities and clinical development contexts.
Apply statistically rigorous approaches to clinical trial data, including survival analysis, longitudinal/mixed-effects modelling, and appropriate handling of missing data and censoring.
Contribute to the scientific and statistical strategy of drug development programs, including the development of predictive biomarkers, novel trial designs, and precision medicine approaches.
Build and maintain well-structured, reproducible, version-controlled analytical pipelines and codebases using Python, R, SQL, and cloud platforms.
Develop and apply data quality frameworks to assess and ensure fitness-for-purpose of diverse data sources for specific analytical questions.
Implement strong model evaluation practices including cross-validation strategies, calibration assessment, and transparent reporting of model performance and limitations.
Build scalable, automated processes for delivering analytical results across multiple programs and data types.
Partner with lead and protocol statisticians in contributing to statistical analysis plans (SAPs) for exploratory data science analyses supporting drug development programs.
Collaborate with cross-functional teams including clinicians, translational medicine scientists, biostatisticians, data engineers, and IT/engineering professionals.
Contribute to team excellence through code reviews, technical mentorship, and raising the overall engineering and methodological standards of the team.
Communicate analytical strategies and results clearly and effectively to both technical and non-technical stakeholders, with strong data presentation and visualisation skills.
Manage and coordinate deliverables across concurrent, fast-paced projects within tight timelines.

Requirements

PhD in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, or Computer Science) with 1+ years of academic/industry experience; or a Master's Degree in a relevant quantitative field with 3+ years of industry experience.
Strong experience in data science and statistical analysis using clinical trial or electronic health records data, particularly in a pharma R&D context.
Experience developing and validating statistical and machine learning models on high-dimensional data for time-to-event, longitudinal, and multivariate outcomes.
Experience in the application of AI/ML and proficiency in Python, R, SQL, and cloud platforms (e.g., AWS, Azure, Databricks).
Familiarity with clinical trial design, drug development processes, and the role of biomarkers in regulatory and clinical decision-making.
A perspective on leveraging innovative approaches to expedite drug development and address the complexities of emerging data types.
Strong problem-solving, collaboration, and communication skills, with the ability to handle several concurrent, fast-paced projects independently and as part of a team.
Experience with genomics, proteomics, imaging, flow cytometry, or immunobiology datasets from clinical trials.
Experience with NLP, causal ML, explainable AI, and survival analysis/time-to-event modelling.
Knowledge of molecular biology and understanding of disease pathways.
Experience with real-world data (RWD/RWE) sources and associated analytical methods.
Familiarity with digital health data and wearable/sensor-derived data types.
Experience with cloud-based scalable compute and deployment patterns for large-scale data processing and model training.

Skills

Data Science
Statistical Analysis
Computational Methods
Patient Segmentation
Biomarker Discovery
Hypothesis Generation
Multimodal Data Analysis
Clinical Trial Data
Omics Data
Genomics
Proteomics
Imaging Data
Flow Cytometry
High-Dimensional Data
Data Integration
Data Mining
Data Visualization
Predictive Modeling
Machine Learning
AI/ML
Deep Learning
NLP
Causal ML
Explainable AI
Survival Analysis
Longitudinal Modelling
Mixed-Effects Modelling
Missing Data Handling
Censoring
Statistical Strategy
Precision Medicine
Data Engineering
Reproducibility
Version Control
Python
R
SQL
Cloud Platforms
AWS
Azure
Databricks
Data Quality Frameworks
Model Evaluation
Cross-validation
Calibration Assessment
Automated Processes
Code Reviews
Technical Mentorship
Data Presentation
Problem-Solving
Collaboration
Communication
Project Management
Real-World Data (RWD/RWE)
Digital Health Data
Wearable/Sensor Data
Cloud Computing
Large-Scale Data Processing
Model Training

Location

Hybrid

Work Type

Hybrid

Experience Level

Senior Manager
1+ years of academic/industry experience (with PhD)
3+ years of industry experience (with Master's Degree)

Education Level

PhD in a relevant quantitative field
Master's Degree in a relevant quantitative field

Benefits

Opportunities for growth
Supportive environment
Inclusion
Wellbeing
Flexibility
Comprehensive benefits

About the Company

Bristol Myers Squibb's purpose-driven culture inspires them to discover, develop, and deliver innovative medicines to prevail over serious diseases.
They offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, and flexibility.
Their work transforms the lives of patients and the careers of those who do it.

Equal Opportunity

BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles.
Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer.
Bristol Myers Squibb is Disability Confident – Employer
BMS will consider qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.