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 support.
Use approved AI tools to accelerate backlog analysis, test-case generation, requirements-to-test traceability, defect summarization, release documentation, and training-content development, with appropriate human review.
Define and support testing for AI-enabled features, including functional performance, accuracy, reliability, fairness, explainability, safety, privacy, security, edge cases, failure modes, and user override or escalation paths.
Confirm that AI-enabled capabilities include appropriate monitoring, feedback loops, human decision points, support procedures, and fallback processes before release.
Capture prompts, assumptions, evaluation evidence, known limitations, and validation decisions, where required, to support auditability and ongoing product improvement.
Stakeholder Alignment & Governance
Build trusted working relationships across business functions, regions, product teams, and technology partners.
Provide concise updates on requirements, decisions, risks, dependencies, open questions, and actions.
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.
Contribute to product reviews, roadmap planning, governance forums, and business readiness discussions.
Translate AI and model concepts, capabilities, limitations, risks, and trade-offs into clear business language that enables informed stakeholder decisions.
Support AI-use-case intake, prioritization, risk assessment, and governance reviews, ensuring proposed solutions use approved tools, data, and processes.
Maintain traceable records of AI-related decisions, evaluation results, data assumptions, approvals, limitations, monitoring requirements, and accountable owners.
Escalate unsupported claims, inappropriate data use, insufficient validation, unacceptable performance, or unclear accountability before an AI-enabled capability advances.
Adoption, Value & Continuous Improvement
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 being used as intended and whether it is solving the defined business problem.
Promote consistent business analysis methods, reusable templates, and strong documentation practices across products.
Identify opportunities to simplify work, reduce manual effort, improve data quality, and scale successful capabilities.
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.
Monitor adoption, user trust, output quality, exception rates, overrides, and realized value; translate findings into backlog, workflow, training, or control improvements.
Gather structured user feedback on AI outputs and workflows to identify where prompts, data, models, interfaces, controls, or operating procedures require adjustment.
Identify repeatable AI-enabled business-analysis practices, prompts, templates, and playbooks that can be reused responsibly across pro

Work arrangement
Hybrid