Data Scientist II at Bristol-Myers… · Hyderabad

Data Scientist II Location: Hyderabad - TS - IN 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 Data Scientist II in the AI Capabilities pod is a hands-on role within BMS’s GenAI Center of Excellence (CoE), delivering GenAI and advanced analytics solutions across Commercial, Operations, Research, Clinical Development, and other functions. The role requires strong foundations in ML/statistical modeling and modern GenAI (LLMs, RAG, vector databases, agentic frameworks), partnering with stakeholders to turn business needs into scalable, production-ready solutions while contributing to CoE standards and knowledge sharing. Key Responsibilities A. GenAI Solution Development - Build GenAI pipelines: Develop end-to-end RAG, LLM workflows, and multi-agent solutions for cross-functional business problems. - Prompt engineering: Design, iterate, and evaluate prompts to improve accuracy and reliability for business use cases. - Vector databases: Implement and manage vector stores for semantic search and knowledge retrieval. - Agentic AI: Build agent workflows with orchestration frameworks to enable multi-step task execution. - Responsible AI: Apply guardrails, evaluation, and fairness considerations; reduce hallucinations and validate outputs. B. Machine Learning & Statistical Modeling - Model development: Design, build, and deploy predictive and generative models across commercial, clinical, operational, and research use cases. - Modeling techniques: Apply regression, classification, clustering, forecasting, and NLP to drive actionable recommendations. - Validation & governance: Support documentation, validation, testing, and maintenance aligned to team standards and regulatory needs. - Experimentation: Design experiments (A/B tests, hypothesis tests, causal analyses) to measure solution performance. C. Data Engineering & Exploration - Data prep: Collect, preprocess, and explore large structured/unstructured datasets from diverse enterprise sources. - EDA: Identify patterns, anomalies, and trends to inform data-driven solutions. - Pipelines: Build and improve scalable data/analytics pipelines with data engineering and platform teams. - Data quality: Partner with data engineering/IT to resolve data issues and ensure reliable modeling inputs. D. Stakeholder Collaboration & Communication - Partnership: Work with stakeholders to define objectives, hypotheses, and KPIs across BMS functions. - Communication: Translate results into clear visuals, reports, and presentations for technical and non-technical audiences. - Problem framing: Help scope ambiguous questions into executable data science and GenAI workstreams. - Stakeholder management: Manage expectations, communicate progress, and escalate risks/blockers as needed. E. Mentorship, CoE Contribution & Continuous Learning - Mentorship: Provide technical guidance, code reviews, and mentorship to junior data scientists. - CoE contribution: Contribute reusable frameworks, toolkits, and documentation to scale GenAI/ML best practices. - Community: Participate in internal sessions, workshops, and hackathons that drive innovation. - Continuous learning: Stay current on GenAI/ML advances and bring practical ideas to the team. Skills & Competencies A. GenAI & Modern AI Skills - LLMs/foundation models: Hands-on knowledge of LLM capabilities, limitations, and enterprise application. - RAG: Ability to design, build, and optimize RAG pipelines for knowledge-intensive use cases. - Prompting: Proficiency in prompt design and structured outputs for reliable LLM behavior. - Orchestration: Experience with tools to build agentic and multi-step AI workflows. - Vector stores: Hands-on experience with vector retrieval for semantic search. - Evaluation & safety: Familiarity with evaluation, hallucination mitigation, validation, and responsible AI principles. B. Core Data Science & Programming - Programming: Strong Python skills; familiarity with R or PySpark is a plus. - Statistics: Strong grounding in inference, regression, forecasting, clustering, and experimental design. - Data: Hands-on SQL and experience with large-scale data; NoSQL/big data exposure is a plus. - ML workflows: Experience training, evaluating, and deploying models with common ML frameworks. C. Cloud, MLOps & Engineering Practices - Cloud: Working knowledge of AWS or Azure for scalable model development and deployment. - MLOps/LLMOps: Awareness of versioning, CI/CD, monitoring, and deployment best practices. - Engineering practices: Familiarity with Git, SDLC, and building APIs for production solutions. - Visualization: Ability to build clear, stakeholder-ready dashboards and visuals. D. Soft Skills & Professional Competencies - Communication: Communicate complex analytics/GenAI concepts clearly to technical and non-technical audiences. - Problem-solving: Break down ambiguous problems into clear, executable workstreams. - Collaboration: Work effectively in cross-functional, matrixed teams. - Mentorship: Coach and support junior team members through reviews and knowledge sharing. - Agility: Prioritize work and adapt quickly across multiple concurrent projects. Experience & Qualifications A. Education - Degree: Bachelor’s or Master’s in a quantitative field (e.g., Data Science, Statistics, CS, Biostatistics, Math, Engineering). - Preferred: Master’s/PhD focused on ML/AI/NLP or applied statistics. B. Professional Experience - Experience: 3–5 years in data science, ML engineering, or applied analytics. - GenAI: Applied experience with LLMs and/or RAG systems (core requirement). - Predictive modeling: Track record of building and deploying ML models (e.g., regression, classification, forecasting, NLP). - Data at scale: Experience working with large structured and unstructured datasets (SQL/Python/cloud). - Delivery: Experience delivering with business and engineering stakeholders in deadline-driven settings. - Preferred: Exposure to biopharma/healthcare and regulated data environments. Good to Have A. Advanced GenAI & AI Capabilities - Fine-tuning: Experience with parameter-efficient fine-tuning (e.g., LoRA/QLoRA/PEFT). - Alignment: Familiarity with RLHF/DPO or related alignment methods. - Multi-modal: Exposure to models combining text with other modalities. - Advanced agents: Experience with advanced agentic frameworks for multi-step workflows. - LLM evaluation: Familiarity with tools and methods for systematic LLM/RAG evaluation. B. Biopharma & Healthcare Domain Knowledge - Healthcare data: Familiarity with real-world and clinical trial data sources. - Standards: Awareness of common healthcare data standards (e.g., HL7/FHIR/CDISC/OMOP). - Regulatory: Understanding of compliance considerations for AI/ML in regulated environments. - Commercial analytics: Exposure to commercial analytics and omnichannel engagement use cases. C. Engineering & Deployment Practices - CI/CD & MLOps: Experience with CI/CD and orchestration for automated ML/GenAI deployments. - Containers: Familiarity with Docker/Kubernetes for packaging and deployment. - Application development: Experience building APIs and lightweight apps to deliver AI insights to users. - Causal inference: Exposure to causal methods for decision support. 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, innovation, productivity, and a positive Company culture. For field-based and remote-by-design roles the ability to physically travel to visit customers, patients or business partners and to attend meetings on behalf of BMS as directed is an essential job function. Supporting People with Disabilities BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to adastaffingsupport@bms.com. Visit careers.bms.com/eeo-accessibility to access our complete Equal Employment Opportunity statement. Candidate Rights BMS will consider for employment qualified applicants with arrest and conviction records, pursuant to applicable laws in your area. If you live in or expect to work from Los Angeles County if hired for this position, please visit this page for important additional information: https://careers.bms.com/california-residents/ Data Protection We will never request payments, financial information, or social security numbers during our application or recruitment process. Learn more about protecting yourself at https://careers.bms.com/fraud-protection. Any data processed in connection with role applications will be treated in accordance with applicable data privacy policies and regulations. If you believe that the job posting is missing information required by local law or incorrect in any way, please contact BMS at TAEnablement@bms.com. Please provide the Job Title and Requisition number so we can review. Communications related to your application should not be sent to this email and you will not receive a response. Inquiries related to the status of your application should be directed to Chat with Ripley. R1601664 : Data Scientist II