Senior Manager, Data Sciences in Denham

Responsibilities

• 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.
• 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.
• Partner with lead and protocol statisticians to contribute 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

Bristol Myers Squibb is an equal opportunity employer and welcomes diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, or any other protected status.