Senior Manager Data Scientist

Role Summary

Senior Manager Data Scientist. You will join a new cutting-edge Drug Development Data Science and Biomarker team to advance the global drug development process. The role requires strong computational, statistical, and biological capabilities with a demonstrated track record of translating complex data into testable hypotheses and experience modeling multimodal (clinical, omics, real-world) data using classical ML and deep learning algorithms. This is a hands-on, state-of-the-art practitioner role focused on biomarker strategy development, predictive modeling, and precision medicine to support clinical drug development. Locations include Cambridge Crossing and Princeton, NJ.

Responsibilities

• Develop and apply novel or existing computational methods for patient segmentation from multimodal clinical and omics datasets for various treatment modalities in partnership with Translational, Clinical and Statistical Scientists
• Partner with lead and protocol statisticians in writing, reviewing and executing protocols and statistical analysis plans (SAP) for biomarkers and diagnostics, highlighting the biomarker strategy for clinical drug development
• Execute biomarker analyses on datasets from BMS clinical trials and real-world data cohorts
• Perform relevant and innovative statistical analyses of high-dimensional (e.g. gene expression, sequencing) data generated by cutting edge technologies
• Execute and contribute to the scientific and statistical strategy of drug development, including development of predictive biomarker(s) and precision medicine
• Optimize and validate biomarker assays for clinical trial usage.
• Develop, implement, and apply state-of-the-art algorithms to address key business problems and drive the implementation of innovative statistical methods in support of biomarker strategy
• Formulate, implement, test, and validate predictive models and implement efficient automated processes for producing modeling results at scale.
• Responsible for collaborating with cross-functional teams, including but not limited to clinicians, data scientists, translational medicine scientists, statisticians, and IT professionals.
• Manage and coordinate resources to produce quality deliverables within timelines for competing priorities.

Qualifications

• Ph.D. in a relevant quantitative field (i.e. Computational Biology, Biostatistics, Statistics, Computer Science, etc.) and 1+ years of academic/industry experience or Master's Degree in a relevant quantitative field and 3+ years of industry experience
• Strong experience in the analysis of data generated by one or more -omics or molecular assays is required
• Knowledge of molecular biology, understanding of disease pathways are preferred
• Strong experience in biomarker data analysis with data generated from clinical trials, or electronic health records
• Experience in modeling methods particularly in their application to pharma R&D; Experience in the application of AI/ML, and proficient in SQL, Python, and R and cloud platforms
• Experience developing statistical and machine learning models on high dimensional and high throughput data for time to event data and longitudinal outcomes
• Perspective in leveraging innovative approaches to expedite drug development and address the complexities of emerging data
• Ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects.
• Strong problem-solving and collaboration skills, and rigorous and creative thinking.
• Excellent communication, data presentation, and visualization skills.
• Capable of establishing strong working relationships across the organization