BureauVeritas | Global AI & Data Transformation

RegionalAI& DataHub Manager

Role Briefing - regional hub leadership, business partnership, delivery accountability, and adoption

Context & Purpose

TheRegionalAI& Data Hubsare wherevalidatedbusiness demand becomesworking,enterprise-gradeAIcapabilities embedded in core business workflows.Each hub pairs business-facing leadership with deep technicalAIleadership: theRegional AI & Data Hub Managerensures the hub is focused on the rightend-to-end journeys, delivers value, buildscapability, and drives adoption theRegional AI & Data Hub Technical Leadensures thatAIsolutions are technically sound,evaluated,reusable, scalable,secure, observable,and aligned to enterprise standards.The hubsoperatewith a field-informed delivery model, groundingprioritiesandsolution designs in direct understanding ofrealuser workflows, operational constraints, adoption barriers, andvalue drivers.Their focus is on transforming end-to-end journeys and ways of workingthrough GenAI, agentic workflows, data science, automation, and enterprise data foundations-not simply delivering isolated use cases.Together, they turn clear, high-value problems into deployedAIcapabilities and reusable enablers for the Shared AI technical foundation.

Role Mission

TheRegional AI & Data Hub Managerleads one of Bureau Veritas's regional AI and data delivery hubsfrom abusiness,solution,delivery, adoption, andteam leadership perspective.The role is accountable forshaping demand with regional and business stakeholders, prioritizing the rightend-to-end journeys rather than isolated use cases, ensuring delivery discipline, building and leading the hub team, and making sure AI-enabled capabilities-including GenAI applications, agentic workflows, data science solutions, automation, and reusable enterprise AI components-aretechnically credible,adopted, operated,evaluated,and continuously improved in real business workflows.

There arethreeRegional AI & Data Hubs- Americas, France, and Asia. Each hub has two key leadership roles:oneRegional AI & Data Hub Managerand oneRegional AI & Data Hub Technical Lead.Thehubsplacebuild capacity close to the businesses and regions of the matrix, while theSharedAI technical foundation,reusable solution archetypes, and governance spineensure thatall threehubs deliverto onecommonenterprisestandard rather than diverginginto separate local approaches.

The Regional AI & Data Hubs work under the technical guidance of theChief Technical Architect, Enterprise AI,theDirector, Data Science,and theDirector, AI Context Fabric & Semantic Platform, ensuring that regional delivery remains aligned with enterprise architecture,data science standards,semantic platform standards, and theSharedAI technical foundation.

Nature of the Role





This is a seniorhubleadership rolefor a business-oriented technology leaderwith meaningfulAIsolutionfluency and practicalunderstanding ofenterprisedata capabilities.It requiresthe ability to translate business priorities into a clear portfolio of AI-enabled journey transformationwork,understandGenAI, agentic, data science, automation, integration, evaluation, and deploymentimplications,engagedirectlywith working prototypes anddelivery teams,lead multidisciplinary teams, manage delivery trade-offs, and ensure thatAIsolutions create measurablevalueafter deployment.

Core Accountabilities

- Demand shaping &AI; solution framing.Partner with business stakeholders to convert priorities into clear problem statements andtransformationopportunities, with a robust focus onend-to-end journeys,business impact, and newAI-enabledways of working rather than isolated use-case delivery.Prioritize work by value, feasibility,data readiness, model and agentic AI suitability,technical complexity, scalability,evaluation requirements, risk,and alignment with the AI strategy, using sufficient AI solution fluency to challenge assumptions, shapeviableopportunities, and guide scale-up decisions.

- Field immersion, workflowunderstanding& valuevalidation.Ground hub priorities and solution designs in direct observation of real user workflows, operational constraints, adoption barriers, and value drivers. Use field insight to sharpen problem statements, keep hub work anchored in operational reality, andvalidatedeployed solutions against adoption, workflow impact, operational outcomes, and business value.

- Portfolio delivery,adoption&AIvalue; realization.Translate validatedjourney-level opportunitiesinto a prioritized hub delivery portfolio in partnership with the Technical Lead, confirming feasibility, solution approach, sequencing, dependencies,evaluation approach, adoption readiness, and delivery risks.Maintain delivery discipline while balancing local business value,reusable AI patterns, scale,human decision boundaries, operating model change, andcontribution to the Shared AI technical foundation.

- Hub team leadership.Build and lead a multidisciplinary hub team across AI, data science, engineering, forward-deployed delivery, and business-facing contributors. Partner closely with the Technical Lead to keep business priorities, solution design, technical feasibility, engineering quality, adoption needs, and enterprise standards aligned,



while fostering a builder culture of experimentation, accountability, and continuous learning.

- Full lifecycle ownership & continuous improvement.Own the business and operational lifecycle of hub solutions, including adoption, stakeholder satisfaction, realized value,operatingmodel fit, and retirement decisions made with the Technical Lead. Ensure performance, reliability, adoption, and business impact are visible through evaluation results and operationalmetrics, anduse those signals to guide continuous improvement and business change actions.

- Forward-deployed engineering.Lead forward-deployed engineers who work with product lines and regional teams to embed solutions into real workflows, adapt them to local operating realities, and accelerate adoption. Use their feedback toidentifyfield friction,recurring edge cases, unmet needs, and opportunities to improve both local solutions and the Shared AI technical foundation.

Profile

Experience:a track recordleadingmultidisciplinaryAI, datascience, data,digital, or technologydelivery teams in an enterprise environment, with accountability for business outcomes, stakeholder alignment, adoption, and scaled delivery.

Mindset & depth:a problem-first mindset that keeps businessvalueand workflow transformationatthecenter ofthe hub'swork strong productand portfoliothinkingmeaningfulAIsolutionfluencyacross AI/ML, GenAI,agentic workflows,datascience, dataengineering, integration,retrieval and context patterns,evaluation,observability,deployment, and lifecycle operationpractical hands-on capability to work with prototypes, prompts, demos, evaluation outputs,telemetry,and delivery teamsthe ability tomake informed trade-offs with the Technical Lead andthe ability to executein complex, multi-stakeholder environments.

Attributes:pragmatic and impact-oriented, a strong collaborator across business and technology, comfortable in ambiguity and iterative delivery, andcommittedtogrounding solution decisionsin realuser workflowsandmeasurable outcomes.For the France hub, professionalFrenchand English language profile varies by hub region.

Positioning, Reporting & Location

Reports to the SVP, GlobalAI&DataTransformation;, with a strong tie to the region served.One Regional AI & DataHubManagerperhub, working as a leadership pair with the Regional AI & Data Hub Technical Lead.Locations: the Americas, France, and Asiahubsrespectively.Occasionaltravel to BV locations.

Overall Mandate

LeadaRegionalAI& Data Hubso it focuses on the rightend-to-end journeys, deliversenterprise-grade AIcapabilities,drives adoptionandmeasurable value, and contributesreusable patterns, field insight, and operational learningsto the SharedAI technical foundationin partnership with the Regional AI & Data Hub Technical Lead.

📌 Regional AI & Data Hub Manager (Noida)
🏢 Bureau Veritas
📍 Noida