Sr Business Analyst, Digital Product Management

Overview

Sr. Business Analyst within Digital Product Management, translating business needs into clear, actionable requirements that enable delivery and adoption of digital, analytics, automation, data science, and AI products across business, product, design, data, engineering, quality, and technology teams.

What you'll do

• 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, validating generated themes 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.
• Create and maintain 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.
• Document responsible AI, privacy, security, quality, regulatory, and data-governance requirements and keep them traceable throughout the product lifecycle.
• 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.
• Help ensure dashboards, analytics, AI outputs, and automation results are explainable and meaningful to business users.
• Evaluate whether a business need is best addressed through AI, traditional analytics, rules-based automation, process redesign, or another solution.
• 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.
• Support backlog refinement, sprint planning, solution reviews, demonstrations, and delivery checkpoints.
• 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 support.
• Define and support testing for AI-enabled features, including accuracy, reliability, fairness, explainability, safety, privacy, security, edge cases, failure modes, and user override or escalation paths.
• Confirm that AI-enabled capabilities include monitoring, feedback loops, human decision points, support procedures, and fallback processes before release.
• Provide concise updates on requirements, decisions, risks, dependencies, open questions, and actions to stakeholders.
• Maintain decision logs, requirement documentation, process artifacts, and other product records in agreed repositories.
• Escalate unclear ownership, unresolved decisions, scope conflicts, and delivery risks early with practical options.
• Translate AI and model concepts, capabilities, limitations, risks, and trade-offs into clear business language.
• Partner with product leaders to define adoption measures, outcome measures, and ways to validate business value.
• Gather user feedback after launch and translate insights into backlog improvements and process changes.
• Monitor whether the product is used as intended and whether it solves the defined business problem.
• Establish baseline measures and track whether AI-enabled capabilities improve cycle time, quality, decision effectiveness, user experience, risk reduction, cost, or other agreed business outcomes.
• Build trusted working relationships across business functions, regions, product teams, and technology partners.
• Champion J&J Values and C.E.O. Leadership Behaviors, and support mentorship, team cohesion, and continuous learning within the team.

What you'll need

• Bachelor's degree in Information Technology, Data Analytics, Computer Science, Engineering or Supply Chain.
• 3+ years of experience as a Business Analyst, Product Analyst, Systems Analyst, or similar role supporting digital, AI and/or data-enabled products.
• Ability to gather requirements, map processes, write user stories and acceptance criteria, and support testing.
• Experience defining business cases, undertaking solution prototyping based on initial business requirements, and tracking value.
• Experience working with cross-functional business and technology teams in a complex, matrixed organization.
• Strong analytical, facilitation, problem-solving, documentation, and stakeholder communication skills.
• Ability to translate technical topics into clear business language and convert business needs into actionable delivery requirements.
• Experience working in iterative product delivery environments and with backlog or work-management tools.
• Digital acumen and demonstrated ability to use approved generative AI or AI-assisted productivity tools, validating outputs before business use.
• Working knowledge of AI and machine-learning concepts, including intended use, data readiness, evaluation, explainability, human oversight, monitoring, limitations, and responsible-use considerations.
• Ability to identify practical AI opportunities, assess business value and risk, and determine when AI is, or is not, the appropriate solution.

Nice to have

• Business, technology, engineering, supply chain, data/analytics, or a related field, or equivalent practical experience.
• Healthcare, Pharmaceuticals, MedTech, Technology, or Supply Chain industry experience.
• Experience with analytics, dashboards, data products, automation, data science, AI, or machine-learning initiatives.
• Experience supporting supply chain, operations, finance, quality, or risk functions in highly regulated business environments.
• Working knowledge of data quality, data governance, reporting logic, system integration, and product adoption practices.
• Experience creating business cases, benefit measures, readiness plans, training materials, and executive-level product updates.
• Relevant business analysis, product management, agile delivery, process improvement, or data certifications.
• Experience supporting AI-use-case discovery, prompt design, rapid prototyping, model or output evaluation, responsible AI reviews, or deployment of AI-enabled workflows.
• Familiarity with generative AI, agentic AI, natural-language processing, predictive analytics, intelligent automation, and AI-enabled business-analysis or product-delivery tools.
• Experience defining AI-specific acceptance criteria and success measures, including output quality, reliability, adoption, human review, monitoring, and measurable business value.

Details

• Location: Bangalore, Karnataka, India.
• Job Function: Data Analytics & Computational Sciences; Sub Function: Business Intelligence; Category: Professional.
• Supports multiple initiatives as needed.