Vice President, Global Analytics & Data Science

Aristocrat Gaming•Las Vegas, NV

7d•$262,028 - $486,623

About The Position

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.

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.

Responsibilities

Strategic Leadership in Analytics 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.
Data Storytelling & Business Enablement 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.