Data Analytics & Computational Sciences

Business Intelligence

Professional

Bangalore, Karnataka, India

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.

· 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

(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.

· Business, technology, engineering, supply chain, data/analytics, or a related field, or equivalent practical experience.

• Healthcare, Pharmaceuticals, MedTech, Technology, Supply Chain

(Include any preferred computer skills, certifications, licenses, languages, etc.)

· Experience with analytics, dashboards, data products, automation, data science, AI, or machine-learning initiatives.

· Experience supporting supply chain, operations, finance, quality, risk function in highly regulated business environments.

· Working knowledge of data quality, data governance, reporting logic, system integration, and product adoption practices.

· Experience in 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.

Advanced Analytics, Analytical Reasoning, Business Behavior, Business Intelligence (BI), Cross-Functional Collaboration, Data Analysis, Data Integration, Data Modeling, Data Privacy Standards, Data Reporting, Detail-Oriented, Management Systems Implementation, Problem Solving, Process Oriented, Project Management, Statistical Analysis Systems (SAS) Programming, Workflow Analysis