Lead Analytics Product & Delivery Manager - Senior Vice President

• Define and own the product strategy, vision, and multi-year roadmap for People Insights and Organizational Effectiveness products.
• Develop and maintain a prioritized portfolio of analytics, reporting, workforce intelligence, and AI-enabled solutions.
• Identify opportunities where data, analytics, machine learning, and Generative AI can improve workforce decisions and organizational effectiveness.
• Evaluate initiatives based on business value, feasibility, regulatory requirements, adoption potential, and strategic alignment.
• Establish product investment priorities and recommend resource allocation across initiatives.
• Drive alignment between business priorities, product strategy, technology investments, and delivery roadmaps.

• Oversee the end-to-end lifecycle of analytics and AI products, from opportunity assessment and discovery through deployment and adoption.
• Partner with Data Engineering, Data Science, and Technology teams to ensure solutions meet scalability, reliability, security, and usability requirements.
• Establish product delivery standards, governance processes, and success measures across the portfolio.
• Ensure that products are supported by clear business requirements, use cases, KPIs, and adoption plans.
• Monitor product performance and business outcomes, making data-driven decisions regarding future enhancements and investments.
• Drive continuous improvement across product development and delivery practices.

• Lead the identification and development of AI-enabled product opportunities across People Insights and Organizational Effectiveness.
• Partner with Data Science teams to incorporate predictive analytics, machine learning, Generative AI, agentic workflows, and decision-support capabilities into products where appropriate.
• Guide build-versus-buy decisions for AI capabilities, platforms, and vendor solutions.
• Establish frameworks for measuring product effectiveness, business impact, user engagement, and AI performance.
• Promote responsible AI adoption while balancing innovation, control requirements, and business outcomes.
• Stay current on emerging trends in AI, workforce technology, analytics platforms, and digital product innovation.

• Build strong relationships with senior leaders across HR, Technology, Risk, Compliance, and the businesses.
• Translate complex workforce and organizational challenges into actionable product strategies and delivery plans.
• Serve as the primary product partner for senior stakeholders seeking to leverage analytics and AI to solve business problems.
• Present product strategies, investment recommendations, portfolio updates, and business outcomes to executive audiences.
• Influence decisions across teams without direct authority and build alignment among diverse stakeholder groups.
• Drive product adoption through effective stakeholder engagement, communication, training, and change-management efforts.

• Lead, coach, and develop a team of product managers responsible for analytics and AI-enabled products.

• Lead, coach, and develop a team of product managers responsible for analytics and AI-enabled products.
• Establish clear goals, accountability, and development plans for team members.
• Build product management capabilities across discovery, roadmap development, prioritization, stakeholder engagement, and product execution.
• Foster a culture of innovation, customer focus, collaboration, and continuous improvement.
• Promote best practices in product management, Agile delivery, experimentation, measurement, and user-centered design.

• Ensure products comply with Citi's model risk, privacy, data governance, cybersecurity, and responsible AI standards.
• Partner with Legal, Compliance, Risk, Model Risk Management, and Technology teams throughout the product lifecycle.
• Ensure appropriate controls, monitoring, documentation, and governance processes are established prior to deployment.
• Maintain oversight of product risks, dependencies, regulatory considerations, and control requirements across the portfolio.

• 10+ years of experience across product management, analytics, technology strategy, consulting, or related disciplines.
• Significant experience leading product teams and managing complex product portfolios within a large enterprise environment.
• Demonstrated success owning product strategy, roadmap development, and delivery of enterprise-scale solutions.
• Proven ability to influence senior executives and drive alignment across business, technology, and control organizations.
• Experience leading large cross-functional initiatives with measurable business outcomes.

• Strong understanding of analytics, data science, machine learning, and AI product development.
• Experience delivering analytics, data, or AI-enabled products from concept through deployment and adoption.
• Familiarity with Generative AI, Large Language Models, Retrieval-Augmented Generation, AI agents, and emerging AI technologies.
• Understanding of data engineering concepts, data platforms, APIs, enterprise architecture, and modern analytics ecosystems.
• Ability to evaluate technical approaches and translate business requirements into scalable product solutions.

• Exceptional problem-solving and structured thinking skills.
• Ability to break down complex and ambiguous challenges into practical, actionable solutions.
• Strong executive communication, storytelling, and presentation skills.
• Experience developing business cases, investment recommendations, and executive-ready decision materials.
• Proven ability to balance strategic vision with operational execution.

• MBA, Master's degree, or other advanced degree from a leading institution.
• Experience in management consulting, technology consulting, or digital transformation environments.
• Experience working in financial services or other highly regulated industries.
• Experience with workforce analytics, workforce planning, talent strategy, organizational effectiveness, or HR technology.
• Familiarity with Workday, people data ecosystems, and HR operating models.
• Exposure to model risk management, responsible AI governance, and enterprise AI deployment practices.