Data Scientist III

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 Summary: Senior technical lead delivering enterprise GenAI and advanced ML solutions across BMS functions. Designs/implements LLM applications (RAG, fine-tuning, agentic workflows) and production LLMOps/MLOps practices. Influences technical standards, mentors DS-1/DS-2 talent, and aligns stakeholders from problem framing to delivery. Typical profile: 5+ years applied DS/ML with strong GenAI depth; biopharma/healthcare domain experience preferred. Key Responsibilities: GenAI Architecture & Solution Leadership Lead GenAI solution design: Architect enterprise LLM apps (RAG, agents, automation) from prototype to production. Fine-tune & adapt models: Apply LoRA/QLoRA/PEFT (and alignment where applicable) for domain use cases. Agentic workflows: Design multi-step orchestration using frameworks such as LangGraph/AutoGen/CrewAI. Evaluation & quality: Own LLM evaluation, hallucination mitigation, and responsible AI standards (e.g., RAGAS/TruLens/DeepEval). Knowledge infrastructure: Govern vector DB/search patterns and knowledge integrations for scalable retrieval. Advanced ML, Modeling & Statistical Expertise Own the model lifecycle: Frame problems, define data strategy, build models, deploy, monitor, and iterate. Advanced modeling: Apply ML/NLP/time series/causal and related methods to ambiguous, highimpact problems. Governance: Lead documentation, validation, and risk controls aligned to responsible AI and regulated needs (e.g., GxP). Experimentation: Design studies (A/B, quasi-experimental) to drive evidence-based decisions. Data Strategy, Engineering & Platform Collaboration Data strategy: Define acquisition, preprocessing, and enrichment for structured/unstructured enterprise data. EDA & insights: Surface patterns and opportunities from multi-domain datasets. Pipelines: Co-design scalable data/ML pipelines with engineering/platform teams. Technical Leadership & Team Development Technical direction: Drive architecture, standards, and engineering best practices across the pod. Mentorship: Coach DS-1/DS-2 via reviews, pairing, and growth feedback. Reusable assets: Build/maintain shared frameworks, accelerators, and templates. Knowledge sharing: Lead workshops, documentation, and community-of-practice efforts. Stakeholder Engagement & Executive Communication Stakeholder partnership: Align senior leaders on priorities, value, and adoption Problem framing: Translate ambiguity into scoped workstreams with KPIs and milestones. Executive communication: Present architectures, results, and recommendations clearly. Delivery leadership: Manage priorities/risks across multiple initiatives in a matrixed environment. Skills & Competencies: GenAI & Advanced AI Expertise LLMs: Production experience deploying and integrating foundation models. RAG: Design and optimize retrieval (hybrid search, reranking, context strategies). Fine-tuning/alignment: Apply LoRA/QLoRA/PEFT and related techniques. Agents: Build orchestrated workflows using common agent frameworks. Responsible AI: Bias/safety controls, evaluation, and governance in regulated settings. Core Data Science, ML & Statistical Skills Programming: Advanced Python; familiarity with distributed tooling as needed. Statistics: Inference, Bayesian methods, experimentation, and causal thinking. ML breadth: Supervised/unsupervised methods; strong model selection and tuning skills. Data at scale: Strong SQL and experience working with large datasets/cloud data services. Cloud, LLMOps & Engineering Excellence Cloud: Deploy ML/GenAI solutions on AWS or Azure in production. LLMOps/MLOps: Versioning, CI/CD, monitoring, and safe rollout patterns. Containers: Docker/Kubernetes for scalable workloads. Engineering: Git/SDLC, APIs, and maintainable production code. Leadership, Communication & Strategic Thinking Communication: Explain complex AI tradeoffs to senior audiences. Strategy: Turn ambiguity into roadmaps with measurable outcomes. Leadership: Set quality bars and drive architectural decisions. Experience & Qualifications: Education: MS/PhD in a quantitative discipline (PhD preferred). Research/applied work: Evidence of strong ML/AI/NLP/statistical project experience. Experience: 5+ years delivering production DS/ML solutions end-to-end. GenAI depth: Hands-on LLMs, RAG, fine-tuning, and agentic workflows (core requirement). Technical leadership: Lead workstreams, mentor others, and make design decisions. Advanced ML: Build/validate/deploy complex models with measurable impact. Cross-functional delivery: Partner across business, engineering, and domain teams to ship. Good to Have: Thought leadership: Publications, talks, or open-source in ML/GenAI. Multi-modal exposure: Experience with vision-language or related models. Knowledge graphs: Familiarity with biomedical ontologies/graphs is a plus. Commercial analytics: Exposure to GTN, market access, targeting, or forecasting. Clinical/RWD: Familiarity with trial data, claims/EHR, registries, and RWE. Regulated environment: Working kn