Data Science Manager

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. 1. Role Summary The Manager, Data Science & AI is a player-coach leader within EIT, AI Cloud & Productivity. The role leads a team delivering GenAI, machine learning, and advanced analytics products for partners across Commercial, Operations, Research, Clinical Development, and other BMS functions. The role's center of gravity is three things: hands-on GenAI/ML delivery, building and developing the team, and serving as the delivery-accountability bridge between US stakeholders and the Hyderabad team. The Manager delivers within the AI capability strategy set by the Senior Manager, owning a focused set of initiatives — the Senior Manager owns the broader portfolio and capability roadmap. 2. Key Responsibilities Core priorities — the role is hired and assessed primarily against A, B, and C. Sections D and E are contributing responsibilities shared with the Senior Manager and platform teams. A. Hands-on Technical Delivery (core) Hands-on data scientist / ML engineer expected to ideate, design, develop, model, and deploy advanced Analytical AI solutions for the enterprise. Stay deeply technical — architecture reviews, code reviews, prototyping, and unblocking the team on complex technical challenges. Apply advanced statistical analysis, ML algorithms, and predictive modelling techniques to extract insights and drive actionable recommendations. Develop and implement predictive models (regression, clustering, time-series forecasting, NLP, causal inference) to solve complex business problems. B. Team Leadership & Culture Building (core) Directly manage a team of ~3–10 data scientists / ML engineers — recruit, mentor, train, and coach. Play a key role in establishing the team culture and capabilities of the pod. Drive hiring, onboarding, performance management, career progression, and capability upskilling in GenAI, MLOps/LLMOps, and responsible AI. Build a culture of curiosity, ownership, collaboration, and hands-on excellence. C. US ↔ Hyderabad Stakeholder Bridge (core) Manage stakeholders in the US and provide clarity to the team in Hyderabad — be the delivery-accountability point across geographies. Translate ambiguous US business asks into structured workstreams, KPIs, and delivery plans the Hyderabad team can execute with confidence. Collaborate with stakeholders to define team vision and capabilities, and deliver high-quality work from Hyderabad. Represent the pod in senior US and Hyderabad forums; build trusted relationships with functional and technical partners. D. Enterprise AI Platform & Capability Development (contributing) Contribute to enterprise-wide Analytical AI capabilities and platforms — including RAG systems, agentic workflows, LLM-based automation, MLOps/LLMOps, and reusable frameworks — within the capability strategy owned by the Senior Manager. Guide architecture trade-offs across retrieval patterns, vector stores, orchestration, evaluation, grounding, and scalability. Drive reuse of shared assets, templates, components, and reference architectures to improve velocity and consistency. Ensure solutions are production-ready, measurable, secure, and aligned to enterprise architecture standards. E. Delivery Excellence & Project Management (contributing) Own delivery for a focused set of initiatives (smaller than the Senior Manager's portfolio) — establish planning, estimation, milestone/sprint reviews, and risk-management rhythms. Define quality gates for validation, testing, documentation, deployment readiness, and post-launch monitoring. Drive project-management rigor — dependency management, risk escalation, and stakeholder reporting. Collaborate with data engineers and IT teams to ensure data availability, quality, and reliability for analysis and modelling. 3. Skills & Competencies A. Hands-on Technical Depth Expertise in programming languages such as Python or R for data manipulation, analysis, and modelling. Expertise in Analytical AI to develop enterprise-wide capabilities / platforms (RAG, agentic workflows, LLMOps, MLOps). Working depth in LLM applications — RAG, prompt/context strategies, grounding, vector retrieval, evaluation, and hallucination mitigation. Strong statistical thinking — experimentation, inference, model evaluation, forecasting, NLP, causal reasoning. Expertise in data visualization tools such as Tableau, Power BI, or matplotlib/seaborn for effective communication of findings. Solid understanding of production-grade delivery: Git, SDLC, APIs, CI/CD, monitoring, versioning, testing, and model/prompt/data lifecycle controls. Experience with cloud-native AI/data solutions on AWS and/or Azure; containerization and scalable deployment patterns preferred. B. People Leadership & Team Culture Demonstrated ability to coach, hire, manage performance, and build culture for a 3–10 person team. Strong project-management and interpersonal skills, with the ability to lead diverse teams and influence cross-functionally. Sound judgment in balancing hands-on technical work with team-leadership responsibilities. C. Cross-Functional Influence & Senior-Leadership Impact Strong verbal/written skills, with the ability to effectively communicate with and strategically impact senior leadership. Ability to manage US stakeholders and provide clarity to the Hyderabad team across time zones. Comfort operating in a matrixed, global environment with multiple functions and evolving priorities. D. Creative Problem-Solving & Business Acumen Strong creative problem-solving skills and business acumen, with the ability to identify key findings from disparate data sources and provide recommendations. Ability to frame ambiguous business problems into solvable AI products. Strong business translation — connecting technical output to user needs, adoption, and measurable outcomes. 4. Experience & Qualifications A. Education & Experience (tiered) Master’s or Ph.D. in Data Science, Computer Science, Statistics, Biostatistics, Applied Mathematics, Engineering, Operations Research, or another quantitative discipline, with 5+ years of experience; OR Bachelor’s in a quantitative discipline with 7+ years of experience in data science, ML/AI engineering, advanced analytics, or closely related applied AI roles. B. Professional Experience People leadership (required): 2+ years of direct management of a team, or sustained formal team-lead responsibility (performance, prioritization, and development of others). Delivery: track record of delivering enterprise-grade solutions end-to-end — from problem framing and data strategy through deployment, adoption, and continuous improvement. GenAI depth: hands-on experience with LLM-based systems, especially RAG, orchestration, evaluation, and production deployment. Stakeholder experience: partnering with US + India business, product, and engineering stakeholders in matrixed environments. 5. Good to Have Biopharma / healthcare domain exposure — commercial analytics, clinical development, real-world data, patient journeys, evidence generation, or regulated workflows. Fine-tuning / adaptation expe