Data Scientist III

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. Role Overview As a Data Scientist III, you will be a strong individual contributor embedded in the Clinical Development analytics function. You will design and build sophisticated ML and AI solutions, conduct rigorous statistical analyses on clinical datasets, and collaborate closely with clinical, medical, and operational stakeholders to translate complex data into actionable insights. This role emphasizes hands-on technical delivery, sound scientific judgment, and the ability to work independently on moderately complex to complex problems. Roles & Responsibilities Analytical Delivery & Modeling Design, develop, and deploy predictive modeling and data science solutions — including regression, clustering, survival analysis, time series forecasting, and Monte Carlo simulation — to address clinical development challenges. Conduct rigorous statistical investigations and exploratory data analysis across clinical datasets including trial data, EHR/EMR, and real-world evidence (RWE). Translate moderately ambiguous scientific or operational problems into structured, hypothesis-driven analytical frameworks spanning study feasibility, trial execution, and patient analytics. Build and maintain data and analytics pipelines — contributing to pipeline design decisions and ensuring computational efficiency within established architectural patterns. Partner with senior data scientists and engineering teams on data quality, pipeline reliability, and scalability improvements. AI & GenAI Solutions Design and implement AI/GenAI-powered solutions — including LLMs, RAG frameworks, and Agentic AI architectures — to augment clinical workflows and decision-making under senior guidance. Apply prompt engineering best practices and contribute to LLM orchestration patterns within the clinical development context. Stay current with emerging AI/ML methodologies and proactively surface opportunities to apply new techniques to clinical problems. Stakeholder Engagement & Communication Collaborate with clinical, medical, and scientific stakeholders to define project objectives, shape data-driven hypotheses, and align on KPIs. Present analytical findings and recommendations to functional leads and cross-functional teams through clear visualizations and structured narratives. Communicate trade-offs between analytical approaches clearly and confidently, with appropriate scientific justification. Engineering & MLOps Practices Apply MLOps and GitOps practices in your own work — ensuring models and pipelines are versioned, documented, and maintainable. Adhere to and actively contribute to engineering standards, code quality, and best practices established by the team. Work within cloud platforms (AWS/Azure), big data technologies (Spark), and version control tooling (GitHub) at scale. Support junior data scientists with technical guidance and peer code review as needed. Skills & Competencies Technical Skills Strong proficiency in Python, PySpark, or R — including clinical and statistical packages and ML frameworks. Solid understanding of experimental design, hypothesis testing, survival analysis, and clinical trial statistical methodologies. Hands-on experience with modern data pipeline and orchestration frameworks (e.g., Kedro, Dagster, Airflow). Practical working knowledge of AI/GenAI technologies — LLMs, RAG frameworks, Agentic AI, and prompt engineering. Experience with agentic AI frameworks such as LangChain, AutoGen, LlamaIndex, or OpenAI Agents in project or near-production settings. Familiarity with SDLC principles; exposure to front-end development (e.g., React) is a plus. Understanding of clinical trial data structures and standards Familiarity with real-world data (RWD) sources including claims, EHR/EMR, and patient registries. Soft Skills Clear and structured communication — able to present complex findings to both technical peers and clinical stakeholders. Strong analytical and problem-solving skills with the ability to navigate ambiguity independently. Collaborative mindset with a drive for scientific rigor and continuous learning. Ability to evaluate and articulate trade-offs between different methodologies with sound judgment. Experience Bachelor's, Master's, or Ph.D. in Data Science, Statistics, Biostatistics, Computer Science, or a related discipline. 5+ years of progressive experience in data science or ML engineering, with meaningful exposure to biopharma, pharma, or clinical research settings. Demonstrated track record of delivering end-to-end analytical or ML solutions — from problem framing through deployment. Experience working with clinical trial data, EHR/EMR, or RWE/RWD sources; Understanding of data governance in regulated environments. Working knowledge of the biopharma drug development lifecycle — clinical phases, regulatory landscape, and data standards. Practical experience with AI/GenAI technologies — LLMs, RAG, Agentic AI, and prompt engineering — applied to real or near-real use cases. Familiarity with model explainability (XAI) and HIPAA compliance considerations in clinical data workflows. Experience contributing to capability assessments or tool evaluations is a plus. Good to Have Hands-on experience with LLM-driven or LLM-augmented workflows in a clinical or drug development context. Experience building and deploying data science applications with a React-based front end and Python/API-driven back end. Familiarity with responsible AI principles — fairness, transparency, and explainability in clinical settings. Contributions to open-source AI/GenAI projects, publications, or participation in relevant hackathons or conferences. Exposure to AI/ML capability roadmap initiatives or center-of-excellence (CoE) contributions. 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. Uniquely Interesting Work, Life-changing Careers With a single vision as inspiring as “Transforming patients’ lives through science™ ”, every BMS employee plays an integral role in work that goes far beyond ordinary. Each of us is empowered to apply our individual talents and unique perspectives in a supportive culture, promoting global participation in clinical trials, while our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues. On-site Protocol BMS has an occupancy structure that determines where an employee is required to conduct their work. This structure includes site-essential, site-by-design, field-based and remote-by-design jobs. The occupancy type that you are assigned is determined by the nature and responsibilities of your role: Site-essential roles require 100% of shifts onsite at your assigned facility. Site-by-design roles may be eligible for a hybrid work model with at least 50% onsite at your assigned facility. For these roles, onsite presence is considered an essential job function and is critical to collaboration, innov