About Bristol Myers Squibb
Bristol Myers Squibb is a global biopharmaceutical company whose mission is to discover, develop and deliver innovative medicines that help patients prevail over serious diseases. We recognize 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.

Shape the Future of Drug Development
Are you a data science leader with the vision to transform complex, multi-modal data into life-changing scientific decisions? Bristol Myers Squibb is looking for an Associate Director of Data Science to join our cutting-edge Drug Development Data Science & Advanced Analytics (DSAA) team. This is a rare opportunity to operate at the intersection of advanced analytics, AI/ML innovation, and clinical science — driving both strategic direction and hands-on execution across early-to-late phase drug development programs. As a senior scientific and technical leader, you will define analytical frameworks, champion methodological standards, and serve as a key partner to cross-functional stakeholders across the organization.

What You'll Do
Data Science Strategy & Scientific Leadership

• Serve as a senior scientific resource within DSAA, providing strategic direction and methodological guidance across multiple drug development programs
• Lead the design and execution of exploratory and confirmatory analyses across diverse and complex data types — from early discovery through late-phase clinical development
• Shape analytical strategy for drug development programs, contributing to decisions around trial design, endpoint selection, biomarker strategy, and evidence generation
• Represent DSAA in cross-functional program team meetings, providing authoritative scientific input and influencing development decisions through rigorous, data-driven analysis

Advanced Analytics & Modelling

• Lead the development and application of novel computational methods for patient segmentation, biomarker discovery, and precision medicine from multimodal clinical and omics datasets
• Drive the integration, mining, and visualization of diverse, high-dimensional datasets across therapeutic areas and development phases — including genomics, proteomics, imaging, and flow cytometry
• Apply and advance the use of AI/ML, deep learning, NLP, causal ML, and explainable AI across multiple data modalities and clinical development contexts
• Lead application of rigorous statistical approaches to clinical trial data, including survival analysis, longitudinal/mixed-effects modelling, causal inference, and principled handling of missing data

Data Engineering & Reproducibility

• Define and champion standards for scalable, reproducible, and well-documented analytical pipelines and codebases using Python, R, SQL, and cloud platforms
• Establish and enforce data quality frameworks to ensure fitness-for-purpose of diverse data sources across programs
• Drive adoption of scalable, automated analytical processes and best-in-class software engineering practices across the team

Leadership, Mentorship & Cross-Functional Influence

• Manage and develop a small team of data scientists, building capabilities and fostering scientific rigor and innovation
• Mentor junior and mid-level data scientists, elevating team-wide methodological and engineering standards through code reviews, collaborative problem-solving, and knowledge sharing
• Partner with lead and protocol statisticians in shaping statistical analysis plans (SAPs) for exploratory data science analyses
• Communicate complex analytical strategies and results with clarity and scientific authority to both technical and non-technical audiences, including senior leadership

What We're Looking For
Required Qualifications:

• PhD in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, or Computer Science) with 6+ years of academic/industry experience; or a Master's Degree in a relevant quantitative field with 8+ years of industry experience
• Demonstrated mastery in data science and statistical analysis using clinical trial or electronic health records data, with a strong track record of delivering impactful results in a pharma R&D context
• Significant experience leading the development and application of statistical and machine learning models on high-dimensional data for time-to-event, longitudinal, and multivariate outcomes
• Proven expertise in AI/ML and proficiency in Python, R, SQL, and cloud platforms (e.g., AWS, Azure, Databricks)
• Deep familiarity with clinical trial design, drug development processes, and the role of biomarkers and data science in regulatory and clinical decision-making
• Demonstrated ability to define and drive analytical strategy across multiple concurrent programs, balancing scientific rigor with practical delivery
• Excellent communication, data presentation, and visualization skills with the ability to convey complex concepts to diverse audiences

Preferred Qualifications:

• 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, including EHR, claims, or registry data
• Familiarity with novel clinical trial designs (e.g., adaptive, platform, or biomarker-enriched trials)
• Prior experience in a people management or formal scientific leadership role
• Experience with cloud-based scalable compute and deployment patterns for large-scale data processing and model training

BMS has an occupancy structure that determines where an employee is required to conduct their work. Site-by-design roles may be eligible for a hybrid work model with at least 50% onsite at your assigned facility. 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.