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The Vice President, Global Analytics & Data Science, will define, scale, and lead the company’s enterprise Analytics-as-a-Service (AaaS) organization, aligned with business objectives and in partnership with the Chief Data and Analytics Officer. This role requires a shown people and service leader who has successfully grown analytics and data science capabilities into a large, high-touch customer-facing organization supporting diverse business needs. The role balances business partnership, service delivery excellence, and technology leadership, with a strong focus on translating complex data into clear dashboards, insights, and narratives that drive action. The VP will be accountable for setting expectations with internal and external customers, establishing scalable service models, and ensuring analytics outputs are trusted, adopted, and impactful. This leader will also define the future vision for analytics, data science, and ML, including how emerging AI capabilities can be responsibly leveraged to increase scale, insight velocity, and decision quality across the enterprise.
Job Responsibility:
Define and implement an enterprise analytics strategy that enables a scalable, customer-facing Analytics-as-a-Service operating model
Establish service models, engagement patterns, prioritization frameworks, and success metrics that balance business demand, delivery capacity, and strategic value
Act as a senior advisor to business leaders and customers, setting clear expectations on scope, timelines, tradeoffs, and outcomes for analytics engagements
Building a long-term vision for analytics evolves the organization from reporting to insight-led decision enablement, including predictive and prescriptive modelling of data and responsible adoption of AI-powered analytics
Promote data storytelling as a core skill in the analytics team, making sure insights are clear, contextual, and ready for decision-making
Establish standards for visualization, narrative flow, and communication with executives across analytics outputs
Ensure analytics leaders and practitioners can turn complex analytical results into business-relevant stories that drive action at all organizational levels
Offer knowledgeable guidance on BI, analytics, and AI platforms, with a solid understanding of what high-quality, scalable analytics look like
Collaborate with data engineering and technology teams to ensure analytics platforms are reliable, user-friendly, and capable of supporting future growth
Define how AI-driven analytics (such as augmented analytics, natural language insights, and automation) can ethically improve scale, efficiency, and customer experience
Requirements:
15+ years of dynamic experience in analytics, data science, or data-driven transformation
5+ years in senior or executive leadership roles within analytics, data science, or data-driven transformation
Consistent record of defining and delivering enterprise analytics strategies that drive measurable business impact
Demonstrated experience building and scaling analytics and data science platforms
Expertise in AI/ML technologies, including predictive modeling, generative AI, and advanced analytics methodologies
Strong understanding of ML Ops practices, including model deployment, governance, monitoring, and lifecycle management
Experience leading global, cross-functional teams spanning analytics, data science, and engineering
Confirmed ability to collaborate effectively with product, technology, and commercial organizations to translate data and AI capabilities into business solutions
Excellent communication and storytelling skills — able to influence C-suite collaborators and translate sophisticated analytics and AI concepts into strategic insights
Strong familiarity with modern data and analytics stacks, cloud platforms (e.g., AWS, GCP, Azure, Snowflake), and visualization tools (e.g., Tableau, Power BI, Looker). (Sql knowledge, conceptual, not daily coding)
Passion for innovation, operational excellence, and responsible AI
Generative AI understanding, LLM capabilities and limitations, Prompting vs fine-tuning vs RAG, Hallucinations, grounding, and trust issues. Automation, AI Enablement & the Future of Analytics
Advanced degree (MS or PhD or equivalent experience or relevant experience) in Computer Science, Statistics, Artificial Intelligence, or related field preferred