• Lead the twice-annual audit process • Oversee documentation compilation and redaction • Develop processes for tracking compliance • Facilitate client-driven technical changes • Create and maintain process documents in Confluence

• Lead the design and deployment of the enterprise AI strategy, prioritizing GenAI, LLM integration, and "Agentic" workflows that automate complex business processes. • Establish the framework for ethical AI, including bias mitigation, data privacy, and security protocols in partnership with Legal and Risk teams. • Develop a "Value Realization" framework to move AI use cases from Proof of Concept (PoC) to full-scale production with measurable ROI. • Lead the rollout of "AI Copilots" and natural-language querying tools, enabling non-technical users to interact with data conversationally. • Direct the data science team in building ML models for demand forecasting, supply chain optimization, and churn prediction. • Ensure all models are monitored for "drift" and maintained for long-term accuracy and reliability. • Serve as the "Single Source of Truth" gatekeeper, ensuring consistent KPI definitions across global business units. • Transition the organization from "request-and-build" static reports to dynamic, self-service environments using Power BI/Qlik. • Oversee the architecture and cost-optimization of the Snowflake Data Cloud. • Direct the use of dbt, Talend, and orchestration tools to ensure data is "AI-ready" (clean, labeled, and accessible). • Act as a primary evangelist for data literacy, helping the organization overcome resistance to AI adoption. • Coach a multidisciplinary team of Data Engineers, BI Developers, and Data Scientists, fostering a culture of rapid experimentation.

• Ensures AI applications, products, and solutions adhere to established company policies, regulatory requirements, and industry best practices • Conducts compliance assessments, performs risk evaluations, and reviews and assesses AI systems to identify potential issues both before and after deployment • Works closely with AI development teams, data scientists, and business stakeholders to support responsible AI practices while documenting findings and recommendations in clear, evidence based, and actionable reports • Performs compliance reviews of AI applications and products to assess alignment with internal policies, governance standards, and standard operating procedures, including verification of required documentation, approvals, and controls prior to production deployment • Conducts structured risk assessments of AI systems across their lifecycle, identifying and documenting risks related to bias, privacy, security, safety, model behavior, and regulatory non compliance; evaluate risk likelihood, impact, and adequacy of mitigation controls • Reviews model development practices, data handling procedures, deployment controls, and technical artifacts (e.g., model cards, system architecture documentation) to identify compliance gaps and discrepancies between documented capabilities and actual system behavior • Investigates AI system incidents, complaints, or governance concerns by analyzing system behavior, data flows, and decision logic; document investigative methods, evidence reviewed, and conclusions reached • Conducts hands on testing and probing of AI systems to validate documented claims regarding performance and behavior, and support ongoing monitoring of deployed systems • Tracks compliance and risk findings, remediation actions, and residual risk through maintained risk registers and supporting documentation; verify corrective actions are implemented and documented • Partners with AI development teams, product owners, and subject matter experts to gather information for assessments and investigations, and prepare clear findings, executive summaries, and recommendations for leadership and governance stakeholders • Monitors trends in compliance and risk findings to identify systemic issues and support continuous improvement of AI governance practices; stay current with evolving AI regulations, standards, and industry best practices • Performs other duties as assigned • Complies with all policies and standards.

• Create innovative, thought-provoking, and highly leveraged “must-have research” content focused on business transformation and the impact of AI adoption, for small and midsize technology providers. • Develop new research and ideas through thought leadership and offer compelling, actionable approaches to client needs and requests that accelerate their ability to act. • Analyze and predict trends in AI technology, AI adoption, and business and go-to-market strategies, providing clients with practical, data-driven recommendations. • Engage directly with small and midsize technology provider Tech CEOs and executive leadership teams through virtual or face-to-face interactions, delivering high-value advice and facilitating strategic discussions on AI Go-to-market effectiveness and business transformation. • Deliver impactful presentations at Gartner events, industry conferences, and client briefings, establishing Gartner’s reputation as an AI leader. • Support research and sales by serving as the voice of the market, collaborating with research teams to create relevant content and driving client engagement on critical priorities. • Peer review research content to ensure quality and relevance for the Tech CEO audience. • Build credibility as an industry expert, representing Gartner research, methodology, and strategy. • Mentor and coach junior team members, fostering a collaborative and innovative research community. • Identify and implement research process improvements to enhance service delivery for small and midsize technology providers. • Be client-centric, proactively engaging with clients to help them leverage Gartner research and insights for tangible business outcomes.