Senior Manager, Data Sciences in London

Are you a hands-on data scientist with a passion for turning complex, multi-modal data into actionable insights that shape clinical decisions? Bristol Myers Squibb is seeking a Senior Manager, Data Science to join our Drug Development Data Science & Advanced Analytics (DSAA) team. This new role is for a senior individual contributor who thrives at the interface of computational science, statistical rigor, and drug development. You will execute and drive exploratory and confirmatory analyses across a rich variety of data types—from clinical trial data to genomics, proteomics, imaging, and beyond—contributing directly to decisions that advance our global development pipeline.

Data Science & Analytics

• Develop and apply novel computational methods for patient segmentation, biomarker discovery, and hypothesis generation from multimodal clinical and omics datasets, in partnership with Translational, Clinical, and Statistical Scientists.
• 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.

Data Engineering & Reproducibility

• 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.

Collaboration & Technical Contribution

• 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.

Required Qualifications

• 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.

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 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.

Equal Employment Opportunity

We are committed to creating an inclusive culture and encourage people of all backgrounds to apply. We provide reasonable accommodations for candidates with disabilities. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, religion, sex, gender, sexual orientation, gender-identity, national origin, age, visual or hearing impairment, medical condition, or other protected status. Data privacy and protection are assured throughout the recruitment process.