•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 products.
•Stay current on emerging AI capabilities and recommend practical experiments that test value quickly, protect users and data, and scale only after evidence supports further investment.
Cultivate and Sustain Collaboration: Partner with cross-functional teams to build trust, open communication, and shared ownership of project goals.
•Champion J&J Values: Model J&J C.E.O. Leadership Behaviors and values to reinforce a healthy and motivating workplace culture.
•Support Team Cohesion: Plan and participate in team-building activities, knowledge-sharing sessions, and feedback loops that foster trust, collaboration, and team cohesion.
•Encourage Growth: Support mentorship, and continuous learning within the team.
Required Minimum Education : Bachelor’s Degree
Required Years of Related Experience: 3+ years of experience
Required Knowledge, Skills and Abilities :
(Include any required computer skills, certifications, licenses, languages, etc.)
•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, mapping processes, writing user stories and acceptance criteria, and supporting testing.
•Experience in 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 to support research, synthesis, requirements development, process analysis, testing, and communications while 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.
