Manager, AI and Data Analytics

Position Summary

The Manager, AI and Data Analytics leads the strategy, design, and delivery of enterprise analytics and AI capabilities that enable data-driven and AI-augmented decision-making across the organization. This role is accountable for building and maturing analytics and AI platforms, managing and developing high-performing data teams, and partnering closely with business leaders to translate strategic objectives into scalable analytics and AI solutions.

The position plays a critical role in advancing business intelligence, operational reporting, and advanced analytics by ensuring data is trusted, accessible, and actionable. This role champions best practices in data architecture, governance, and visualization while driving continuous improvement and innovation across analytics services.

Recognizing that trusted data is the foundation for effective AI, this role also leads the responsible adoption of AI capabilities, including predictive machine learning, forecasting, and AI features embedded in modern BI platforms to generate insight, accelerate decision-making, and deliver measurable business value. This role balances strategic direction with technical fluency, guiding the team through model selection, deployment, and ongoing performance monitoring while ensuring AI solutions are explainable, ethical, and aligned with enterprise governance standards.

Responsibilities

• Lead & Develop Talent: Manage, coach, and grow a high-performing team of data analysts, data engineers, and BI professionals. Establish clear goals, drive accountability, and build long-term team capability

• Own the Analytics & AI Strategy: Define and execute a roadmap aligned to business priorities—balancing immediate reporting needs with long-term platform scalability and maturity

• Drive Business Impact: Partner directly with cross-functional leaders to identify opportunities where analytics and AI can improve performance, reduce cost, and enable strategic decision-making

• Deliver Scalable Solutions: Oversee the end-to-end design and delivery of analytics platforms, dashboards, data models, and reporting solutions that are secure, high-performing, and user-friendly

• Improve Data Quality & Accessibility: Lead data integration efforts across multiple sources, establish data quality standards, and resolve root-cause data issues to ensure trusted, reliable insights

• Champion Data Governance: Enforce enterprise data governance, privacy, and security standards while ensuring compliance with regulatory and audit requirements

• Operationalize Excellence: Own platform performance, monitoring, and support models—driving continuous improvement through automation, performance metrics, and user feedback

• Manage Projects & Portfolio: Oversee multiple concurrent initiatives, managing scope, timelines, resources, risks, and stakeholder communication to ensure on-time, high-quality delivery

• Advance AI Capabilities: Lead the development and deployment of predictive models and forecasting solutions; drive adoption of AI-powered features within BI tools (e.g., Copilot, automated insights)

• Establish AI Best Practices: Implement standards for model lifecycle management (build, validate, deploy, monitor, retrain) while ensuring solutions are explainable, ethical, and aligned with governance standards

• Drive Innovation: Stay ahead of emerging trends in analytics and AI, evaluate new technologies, and continuously improve tools, processes, and delivery capabilities

Minimum Qualification

• Bachelor’s degree in information systems, Computer Science, Data Science, Business Analytics, or related field.
• 7+ years of experience in data analytics, data engineering, or business intelligence, including leadership or supervisory experience.
• Proven track record implementing and managing enterprise analytics platforms, data warehouses, and advanced analytics solutions.
• Hands-on experience with analytics and BI tools (e.g., Power BI, Tableau, Qlik, OBIEE), SQL, and modern data platforms (e.g., Snowflake, Databricks, Azure, AWS, or GCP).
• Experience with data governance, MDM, and enterprise data management frameworks.
• Demonstrated experience leading or delivering AI and machine learning initiatives, including predictive models, forecasting, or AI features embedded in BI platforms, with working knowledge of model lifecycle management and responsible-AI practices.
• Strong project management and communication skills for both technical and executive audiences.
• Master’s degree (MBA, MIS, or related).
• Familiarity with ITIL, TOGAF, or other enterprise architecture and service management frameworks.