Associate Director, Data Science

Bristol Myers Squibb•Princeton, NJ

1d•$189,670 - $229,834•Hybrid

About The Position

This is a new position. You will join a cutting-edge Drug Development Data Science and Advanced Analytics (DSAA) team as a senior scientific and technical leader, driving data science strategy and execution to advance the global drug development process. We are looking for a seasoned data scientist with a strong computational, statistical, and biological background and a demonstrated track record of leading analytical strategy, driving methodological innovation, and translating complex, multi-modal data into impactful scientific insights that inform clinical development decisions. As an Associate Director, you will provide scientific leadership across diverse data types generated in drug development — including clinical trial data, genomics, proteomics, imaging, flow cytometry, and other biomarker modalities — driving both the strategic direction and hands-on execution of data science efforts across early-to-late phase drug development programs. You will define and champion analytical frameworks, methodological standards, and scalable approaches that elevate the quality and impact of data science across the organization, while serving as a key scientific partner to Biostatistics leads, Translational and Clinical Scientists, and senior cross-functional stakeholders. This position may include management of a small team of data scientists. We are looking for a technically excellent, scientifically influential, and strategically minded practitioner.

Requirements

Ph.D. in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, Computer Science, or related field) and 6+ years of academic/industry experience; or Master's Degree in a relevant quantitative field and 8+ years of industry experience
Demonstrated mastery in data science and statistical analysis with data generated from clinical trials or electronic health records, 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 the application of 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
Significant track record of driving statistical and AI/ML innovation, with a perspective on leveraging emerging approaches to expedite drug development and address complexities of novel data types
Demonstrated ability to lead, mentor, and collaborate with multidisciplinary teams, and to manage multiple concurrent high-priority programs with competing timelines
Excellent communication, data presentation, and visualization skills; ability to convey complex analytical concepts to diverse audiences including senior leadership
Capable of establishing and sustaining strong, high-trust 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
Experience with real-world data (RWD/RWE) sources, including EHR, claims, or registry data, and associated analytical and causal inference methods is preferred
Familiarity with digital health data and wearable/sensor-derived data types is a plus
Experience with or exposure to novel clinical trial design (e.g., adaptive, platform, or biomarker-enriched trials) is preferred
Prior experience in a people management or formal scientific leadership role is a plus
Experience with scalable compute and deployment patterns, including cloud-based platforms and parallelization for large-scale data processing and model training is a plus

Responsibilities

Serve as a senior scientific resource within the DSAA organization, providing strategic direction and methodological guidance on data science approaches across multiple drug development programs
Lead the design and execution of exploratory and confirmatory analyses (both hypothesis-generating and hypothesis-driven) across diverse and complex data types, from early discovery through late-phase clinical development
Drive the development and implementation of innovative statistical methods, novel analytical frameworks, and state-of-the-art AI/ML approaches to address key scientific questions in drug development
Shape the analytical strategy for drug development programs, contributing to decisions around trial design, endpoint selection, biomarker strategy, and evidence generation
Identify opportunities to leverage emerging data science methodologies and technologies to accelerate drug development and address the complexities of novel data types
Represent DSAA in cross-functional program team meetings, providing authoritative scientific input and influencing development decisions through rigorous, data-driven analysis
Lead the development and application of novel computational methods for patient segmentation, biomarker discovery, and precision medicine from multimodal clinical and omics datasets in partnership with Translational, Clinical, and Statistical Scientists
Oversee and 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
Drive the integration, mining, and visualization of diverse, high-dimensional, and disparate datasets across therapeutic areas and development phases, developing novel analytical approaches where existing methods fall short
Lead the formulation, implementation, testing, and validation of predictive models and scalable automated processes for delivering modeling results across multiple programs
Apply and advance the use of AI/ML, deep learning, NLP, causal ML, and explainable AI across multiple data modalities and clinical development contexts, maintaining currency with the state of the art
Lead application of rigorous statistical approaches to clinical trial data, including survival analysis, longitudinal/mixed-effects modeling, causal inference, and principled handling of missing data and censoring
Contribute to and influence the scientific and statistical strategy of drug development programs, including the development of predictive biomarkers, novel trial designs, and precision medicine approaches
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 assess and ensure fitness-for-purpose of diverse data sources across programs
Promote rigorous model evaluation practices including appropriate cross-validation, calibration assessment, out-of-sample validation, and transparent reporting of model performance
Drive adoption of scalable, automated analytical processes and best-in-class software engineering practices across the team
If applicable, manage and develop a small team of data scientists, building capabilities, fostering scientific rigor and innovation, and ensuring delivery of high-quality outputs within program timelines
Mentor and provide technical guidance to 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 supporting drug development programs
Collaborate with and influence cross-functional teams including clinicians, translational medicine scientists, biostatisticians, data engineers, regulatory scientists, and IT/engineering professionals
Communicate complex analytical strategies and results with clarity and scientific authority to both technical and non-technical audiences, including senior leadership
Build and maintain strong, high-trust working relationships across the organization, establishing DSAA as a valued scientific partner

Benefits

Medical, pharmacy, dental, and vision care
BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP)
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
Paid Time Off
flexible time off (unlimited, with manager approval, 11 paid national holidays)
160 hours annual paid vacation for new hires with manager approval, 11 national holidays, and 3 optional holidays
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
an annual Global Shutdown between Christmas and New Years Day