Sr Business Analyst, Digital Product Management

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com . As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Job Function: Data Analytics & Computational Sciences Job Sub Function: Business Intelligence Job Category: Professional All Job Posting Locations: Bangalore, Karnataka, India Job Description: Role Summary The Sr. Business Analyst, Digital Product Management translates business needs into clear, actionable requirements that enable successful delivery and adoption of digital, analytics, automation, data science, and AI products. The role works across business, product, design, data, engineering, quality, and technology teams to define problems, clarify scope, improve processes, support delivery, and confirm that product outcomes meet business expectations. This role supports multiple initiatives as needed while maintaining strong traceability from business objectives and user needs through requirements, testing, launch, adoption, and value realization. Business Discovery & Problem Definition · Partner with business stakeholders and product managers to understand goals, user needs, pain points, risks, and desired outcomes. · Plan and lead discovery sessions, interviews, workshops, and process reviews with business and technical teams. · Document the current state, identify root causes and gaps, and define a clear future-state vision. · Convert broad ideas and requests into well-defined business problems, product opportunities, and measurable outcomes. · Identify assumptions, dependencies, constraints, policy considerations, data needs, and stakeholder impacts early. · Use approved AI tools to synthesize stakeholder interviews, workshop notes, user feedback, and process documentation; validate generated themes, assumptions, and conclusions with stakeholders before use. · Identify and assess AI, analytics, and automation opportunities based on business value, user need, data readiness, feasibility, risk, and required human oversight. · Apply AI-assisted research and scenario analysis to accelerate root-cause analysis, generate hypotheses, and compare future-state options while maintaining professional judgment and source traceability. Requirements & Product Definition · Create and maintain clear business requirements, functional requirements, user stories, acceptance criteria, process flows, business rules, and data definitions. · Ensure requirements are understandable, testable, prioritized, and linked to business objectives and product outcomes. · Facilitate requirement reviews and secure alignment across business, product, data, engineering, quality, and technology partners. · Maintain end-to-end traceability from approved requirements through design, development, testing, release, and business validation. · Assess change requests, clarify impact, and support product leaders with scope and priority trade-offs. · Use approved AI capabilities to accelerate drafting and refinement of requirements, user stories, acceptance criteria, process flows, business rules, data definitions, and prototypes; review all outputs for accuracy, completeness, and alignment. · Document responsible AI, privacy, security, quality, regulatory, and data-governance requirements and ensure they remain traceable throughout the product lifecycle. Process, Data & Insight Analysis · Analyze business processes, workflows, system interactions, and data flows to identify improvement opportunities. · Define business-friendly data terms, calculation logic, inclusion and exclusion rules, and reporting expectations. · Partner with data and engineering teams to evaluate data availability, quality, lineage, and readiness for product use. · Use evidence and structured analysis to support product decisions, prioritization, and solution recommendations. · Help ensure dashboards, analytics, AI outputs, and automation results are explainable and meaningful to business users. · Use AI-assisted process mining, pattern detection, text analysis, and data exploration to identify bottlenecks, trends, anomalies, and opportunities to redesign work. · Evaluate whether a business need is best addressed through AI, traditional analytics, rules-based automation, process redesign, or another solution, avoiding the use of AI where it does not create meaningful value. · Partner with data science and engineering teams to define representative data, data-quality thresholds, evaluation criteria, bias considerations, and validation evidence for AI-enabled capabilities. · Critically evaluate AI-generated insights for accuracy, relevance, explainability, and potential bias before translating them into recommendations or decisions. Delivery, Testing & Release Readiness · Support backlog refinement, sprint planning, solution reviews, demonstrations, and delivery checkpoints. · Clarify requirements and resolve questions quickly during design, development, and testing. · Create business test scenarios and support user acceptance testing, defect triage, retesting, and sign-off. · Confirm that delivered features meet acceptance criteria, business rules, data expectations, and intended user outcomes. · Support release readiness, training content, user guidance, communications, and transition to ongoing suppo