Senior Manager, Data Science

Job description

Working with Us Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us. Senior Manager, Data Science Department: Data Science and Advanced Analytics (DSAA) Position Summary This is a new position. You will join a cutting-edge Drug Development Data Science and Advanced Analytics (DSAA) team to advance the global drug development process. We are looking for a candidate with strong computational, statistical, and biological capabilities and a demonstrated track record of translating complex, multi-modal data into testable hypotheses and actionable insights in support of clinical development activities and decisions. As a hands-on individual contributor, you will drive exploratory and confirmatory analyses (both hypothesis-generating and hypothesis-driven) across diverse data types generated in drug development, including clinical trial data, genomics, proteomics, imaging, flow cytometry, and other biomarker modalities. You will define and implement approaches, processes, algorithms, and pipelines that support the analytics, visualization, and decision support needs of drug development scientists and project teams, while collaborating closely with Biostatistics leads, Translational and Clinical Scientists, and cross-functional partners across the organization. We are looking for a hands-on, state-of-the-art practitioner. What You'll Do Data Science & Analytics Develop and apply novel or existing 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 novel ways of integrating, mining, and visualizing diverse, high-dimensional, and disparate datasets generated across early-to-late phase drug development Formulate, implement, test, and validate predictive models and implement efficient automated processes for producing modeling results at scale Perform relevant and innovative statistical analyses of high-dimensional data (e.g., gene expression, sequencing, imaging features) generated by cutting-edge technologies 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 modeling, 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 evaluation practices including appropriate 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 via code reviews, technical mentorship, and raising the overall engineering and methodological rigor of the team Communicate analytical results clearly and effectively to both technical and non-technical stakeholders, with strong data presentation and visualization skills Manage and coordinate resources to produce quality deliverables within timelines for competing priorities Build and maintain strong working relationships across the organization Key Requirements Ph.D. in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, Computer Science, or related field) 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 data science and statistical analysis with data generated from clinical trials or electronic health records, particularly in application to pharma R&D Experience in 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 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 Preferred Qualifications 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 and associated analytical methods is preferred Familiarity with digital health data and wearable/sensor-derived data types 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 If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career. Compensation Overview: Warsaw - PL: zł354,470 - zł429,541 The starting compensation range(s) for this role are listed above for a full-time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. The starting pay takes into account characteristics of the job, such as required skills, where the job is performed, the employee’s work schedule, job-related knowledge, and experience. The final compensation will