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
On-site Protocol
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. Bristol Myers Squibb is Disability Confident – Employer.
#J-18808-Ljbffr…