Business Analyst - Data & AI Translator

Key Duties and Responsibilities:

Demand translation and framing

• Engage business owners to surface problems, decisions and pain points, and translate them into clearly framed analytical or AI use cases with a defined decision, user and expected action.
• Write business requirement documents, use-case briefs, success criteria and acceptance tests that engineering, BI and data science teams can build against.
• Challenge requests that have no decision owner, no measurable outcome or no viable data source, and reframe or decline them.

Delivery coordination

• Coordinate the Data Engineering, BI & Context Engineering, and Data Science & AI teams through the delivery of each assigned use case.
• Validate outputs against business logic, definitions and edge cases before release, and run user acceptance with the business.
• Escalate blockers, dependencies and scope changes to the Portfolio Manager and the relevant Capability Head.

Analysis and insight

• Perform domain analysis on assigned data products — commercial, operational, or product and lifecycle, depending on the assigned scope.
• Interpret dashboards, metrics, forecasts and model outputs for the business and convert them into recommended actions.
• Build and maintain domain scorecards, exception reporting and periodic operating-review material.

Adoption and benefit realization

• Train and coach business users on dashboards, Genie Spaces, forecasts and AI applications in the assigned domain.
• Track usage and adoption, identify where a delivered product is not being used, and act on the cause.
• Measure the business impact of delivered work against an agreed baseline and report it to the business owner and the Portfolio Manager.

Business context stewardship

• Maintain the business glossary, metric definitions and decision rules for the assigned domain, in partnership with the BI & AI Context Engineer.
• Document domain exceptions, seasonality, data caveats and known quality issues so they are reflected in analytics and AI answers.

Key Deliverables

• Use-case briefs and business requirement documents with defined decisions, users, success criteria and benefit owners.
• Prioritized domain demand pipeline, reviewed with the business owner on an agreed cadence.
• Domain scorecards, exception reports and operating-review packs.
• User acceptance sign-offs and adoption and training records.
• Benefit-tracking reports comparing post-implementation performance against baseline.
• Maintained domain glossary, metric definitions and documented business rules.

Accountability and Success Measures

• Quality and completeness of requirement framing — the technical teams should not have to guess the business intent.
• Business relevance and correctness of delivered analytics and AI outputs in the assigned domain.
• Adoption rates of delivered products by the intended users.
• Documented business impact of delivered work against baseline.
• Accuracy and currency of domain definitions and business rules.

Working Relationships

• Internal: assigned business owners and operating teams; BIDA Business Partner Head; Data & AI Portfolio Manager; BI & AI Context Engineers; Data Engineers; Data Scientists; AI / LLM Engineers; Data Governance Specialist.
• External: none routinely; vendor demonstrations and benchmarking as directed.

Qualifications

• Bachelor's degree in Business, Economics, Industrial Engineering, Statistics, Information Systems, Computer Science or a related field. Post-graduate study or a business-analysis certification is an advantage.
• Three or more years in business analysis, commercial or operations analytics, or a domain role with heavy data use. Experience acting as an intermediary between business and technical teams is required. Prior exposure to the assigned domain (retail and food operations, battery manufacturing, supply chain, or commercial) is strongly preferred.
• Optional: CBAP or equivalent business-analysis certification, Databricks fundamentals, Power BI certification, Lean Six Sigma.

Technical Skills

• SQL to an independent working level; able to profile and validate data without engineering support.
• Working knowledge of Databricks, Power BI and natural-language analytics such as Genie Spaces.
• Requirements documentation, process mapping and use-case specification.
• Basic statistical literacy — distributions, variance, correlation versus causation, sampling and significance.
• Ability to read and sanity-check forecast and model outputs, without necessarily building them.
• Spreadsheet modelling and business-case construction.

Behavioural Competencies

• Business acumen — understands how the assigned domain makes and loses money.
• Structured problem framing and critical thinking.
• Influence and stakeholder management without direct authority.
• Clear written and verbal communication to both executive and operating audiences.
• Bias for adoption — treats a delivered dashboard that nobody uses as a failure.
• Intellectual honesty in reporting benefit and in retiring work that is not delivering.