Associate Director, Data Science

• 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

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
• Capable of establishing and sustaining strong, high-trust working relationships across the organization

• Experience with

from clinical trials is highly preferred

• Experience with

is highly preferred

• Experience with

and time-to-event modeling is highly preferred

• Experience with

is highly preferred

• Knowledge of molecular biology and understanding of disease pathways is preferred
• Experience with

sources, including EHR, claims, or registry data, and associated analytical and causal inference methods is preferred

• Familiarity with

and wearable/sensor-derived data types is a plus

• Experience with or exposure to

(e.g., adaptive, platform, or biomarker-enriched trials) is preferred

• Prior experience in a

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

We hire for skills and capabilities, not just credentials - if this role excites you, but doesn't perfectly match your resume, we encourage you to apply anyway.