Senior Manager – AI, Data, and Analytics

• Serve as a first point of contact for Corporate Functions on AI, Data, and Analytics tooling and capabilities, advising on best-fit solutions (enterprise platforms vs. productivity-embedded solutions) based on business use cases and risk considerations.
• Help operationalize intake-to-value delivery for AI/GenAI use cases (intake → assess data readiness → pilot → scale), including clear stage gates, ownership, and benefits tracking in partnership with business, risk, and engineering stakeholders.
• Partner with business leaders and BRMs to shape demand and translate priorities into data domains, analytics use cases, and measurable business outcomes.
• Incorporate data and GenAI initiatives into prioritized roadmaps with clear outcomes, dependencies, and data readiness, ensuring alignment with organizational strategic priorities.
• Define and enable agentic AI (e.g., copilots, task-oriented agents, and workflow-embedded AI) by establishing the data, architecture, and governance foundations that allow AI agents to safely reason, act, and integrate with enterprise systems.
• Partner with business leaders and external vendors establishing a data and analytics strategy and operating model (intake, prioritization, delivery, adoption, and value realization) for each function, including governance models that ensure scalable, AI-ready solutions.
• Evaluate and guide adoption of established and emerging technologies (e.g., cloud data platforms, vector databases, retrieval-augmented generation (RAG), advanced analytics, and real-time inference architectures) to enhance corporate capabilities.
• Promote enterprise reporting standards and governed semantic layers to ensure consistent definitions of key metrics across Corporate Functions.
• Improve reliability and trust through data observability and operational controls (monitoring, alerting, SLAs, and automated checks) in collaboration with platform and integration teams.
• Identify risks and support mitigation strategies impacting financial and operational performance.
• Communicate complex data, architecture, and analytics concepts to non-technical stakeholders in a clear, actionable manner.
• Continuously evaluate and improve data and analytics processes and methodologies to increase reliability, usability, and time-to-insight.
• Allocate resources effectively to ensure successful execution of data and analytics initiatives.
• Track and report on the performance, adoption, and realized value of data and analytics products and initiatives (e.g., dashboards, regular stakeholder reviews, and post-implementation lessons learned.)

• Minimum of 7–10 years of experience in data analytics.
• Strong technical foundation in data architecture and analytics, including experience with data visualization tools (e.g., Power BI, Tableau) and advanced analytics tooling (e.g., SQL, Python, R).
• Demonstrated experience defining and governing enterprise data architecture, including data integration, data quality, metadata, lineage, and data governance frameworks.
• Strong experience with modern data delivery practices such as Data Ops (CI/CD, automated testing, monitoring/observability, and documentation) in support of regulated, auditable environments.
• Knowledge of AI, machine learning, and GenAI applications in business and financial analytics.
• Strong business acumen with the ability to translate complex analyses into actionable recommendations.
• Proven leadership skills, including experience influencing senior stakeholders and leading cross-functional teams.
• Bachelor’s degree in data science, Statistics, Computer Science, Business Administration, or a related field.

Demonstrates expertise in data architecture, analytics, and AI applications, with a strong focus on operationalizing data initiatives and delivering measurable business outcomes. Proven ability to communicate complex concepts to diverse stakeholders and lead cross-functional teams in a data-driven environment.

• Data Architecture
• Data Visualization Tools
• Data Ops Practices
• AI Applications in Business
• Leadership and Stakeholder Influence

• Data Analytics
• SQL
• Python
• R
• Data Integration
• Data Quality
• Metadata Management
• Data Governance
• Advanced Analytics
• Data Delivery Practices

• Business Acumen
• Communication Skills
• Cross-Functional Team Leadership

• Data Governance Frameworks
• Data Observability
• Operational Controls
• Enterprise Reporting Standards
• Risk Mitigation Strategies

• Power BI
• Tableau
• Cloud Data Platforms
• Vector Databases
• Retrieval-Augmented Generation