Senior Manager, Data & AI Strategy

Minor Hotels Europe and Americas

Senior Manager role driving Data & AI strategy for clients at Capgemini Invent, blending creativity and technology expertise.

Posted 5/23/2026full-timePune • 🇮🇳 IndiaSeniorWebsite

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Hard Skills

data governancedata architecturedata qualitymachine learningAI strategydata pipelinescloud technologiesdata catalogue toolsmodel evaluationdata privacy

Soft Skills

advisory skillsstakeholder managementleadershipcommunicationstorytellingpresentation skillsmentoringfacilitationstrategic thinkingproblem-solving

Tools & Technologies

GCPAzureAWSAlationCollibraIDQAtaccamaDatabricksdata Lakehouse architecturesGenAI tools

Certifications & Qualifications

Bachelor's degree in computer scienceMBAcloud technology certificationsDAMA certificationDCAM certificationTOGAF certificationResponsible AI FrameworksISO 42001 certificationAI/ML certificationsdata privacy certifications

Industry Keywords

LifesciencesCPGBankingInsurancesupply chaintelecomenergy utilitiesEU AI ActGDPRNIST AI RMF

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonUnity

About the role

Key responsibilities & impact

Conduct comprehensive current state analysis of existing data systems, processes, and frameworks of an organization
Prioritize Data and AI investments based on value, feasibility, risk, domain and BU needs
Define the vision for AI-driven data capabilities; align them with enterprise strategy
Ability to advise C-suite and business leaders on AI adoption, risks and mitigation
Measure impact using both model performance and business value metrics, ensuring AI initiatives deliver ROI
Identifying and defining the AI Use cases in the domain of data strategy, data governance, data architecture, data quality, MDM etc
Conduct market, competitive, and trend analysis to inform strategic decisions
Drive Data Maturity/AI Maturity assessments
Drive CXO conversations effectively
Convert business objectives into data requirements, ML problem definitions, and measurable success metrics (precision/recall, business KPIs)
Identify required training datasets, data quality thresholds, labeling needs, and privacy constraints across the AI lifecycle
Conducting stakeholder workshops (Risk, Legal, Compliance, Product, Tech)
Lead and facilitate engagements in data governance, establishing policies, procedures, and frameworks to ensure metadata, master data, data quality, security, and compliance
Drive the foundational pillars of AI data strategy—data quality, data governance, Data architecture, MDM, data privacy, security, regulations, ML Models, AI use cases etc
Drive RFI/RFP and presales engagements
Lead and contribute to offerings development, provide thought leadership
Utilize industry contextual knowledge to derive domain specific use cases for analytics or AI/ML applications
Ensure AI experiences integrate seamlessly with semantic models, data Lakehouse architectures, and enterprise ingestion/curation frameworks, AI Architecture, metadata systems
Embed governance, privacy, ethical guardrails, and explainability into AI workflows
Define Governance and Privacy frameworks for AI workloads
Guide best practices on data privacy, contracts, and controls
Own overall coordination with business and delivery stakeholders from a functional perspective
Manage and mentor a team of consultants and execute people management activities

Requirements

What you’ll need

Bachelor’s degree in computer science, Information Technology, or any related field
preferred MBA from a tier 1 and tier 2 college
Proven experience in advisory and consultancy roles focused on Data and AI strategy
At least 1 end to end consulting engagement experience
Proficiency in cloud technologies, particularly GCP, Azure, and AWS, with relevant certifications preferred
Understanding of Data Catalogue tools like Alation, Collibra, Unity Catalog etc
Understanding and experience of working on Data Quality and DQ dimensions, metrics, tools (IDQ, Ataccama etc)
Strong understanding of data governance, domain models, and modern data architectures (Fabric, Azure, data mesh, Purview etc), AI Architecture, GenAI Tools, GenAI Tech stack (Azure, AWS, GCP, Databricks etc)
Experience working with AI/ML teams and familiarity with tools (e.g., Python basics, prompt engineering, model evaluation)
Knowledge of ML lifecycle, model metrics, data pipelines, and Gen AI patterns
Supervised/unsupervised learning, LLMs, RAG, embeddings, Agentic AI
ML Lifecycle-Training, evaluation, deployment & monitoring
Strong knowledge of DAMA, DCAM and TOGAF frameworks and principles
Certifications preferred
Understanding of data regulations like EU AI Act, NIST AI RMF, GDPR, CCPA, and sector-specific laws (HIPAA, DPDP Act in India)
Responsible AI Frameworks from Big Tech (Google, Microsoft etc.), ISO standards related to AI (e.g. ISO 42001 for AI Management systems)
Domain expertise-Lifesciences, CPG, Banking, Insurance, supply chain, telecom, energy utilities etc. domain expertise to contextualize data models, KPIs, and AI use cases
Should be a good storyteller, presenter and capable of steering leadership communication.

Benefits

Comp & perks

Flexible work arrangements
Career growth programs
Certifications in the latest technologies