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The Sr. Director, AI Transformation is a newly created, enterprise-wide leadership role responsible for designing, championing, and executing Ecovyst’s artificial intelligence (“AI”) strategy. The role exists to translate AI from a technology concept into measurable business outcomes — accelerating productivity, delivering durable cost savings, and strengthening Ecovyst’s competitive position.

This is a business-facing, results-oriented role positioned outside of Information Technology and reporting directly to the CEO. The independence of this role is intentional: the Director must possess the credibility, judgment, and personal style to navigate that friction professionally — partnering productively with IT while protecting the pace and independence of the AI agenda.

• Develop and own the AI strategy. Draft a three-year enterprise AI strategy with clear business objectives, prioritized use-case portfolio, investment plan, and measurable financial and operational outcomes.
• Maintain a dynamic opportunity pipeline. Build and maintain a rolling backlog of AI use cases across the enterprise, prioritized by value, feasibility, strategic fit, and time-to-impact.
• Define the operating model. Establish how AI initiatives are identified, funded, governed, piloted, scaled, and sustained across Ecovyst, including clear accountabilities between the AI function, IT, and business units.
• Benchmark continuously. Monitor AI developments across specialty chemicals, industrial services, and adjacent sectors to ensure Ecovyst remains competitive and identifies emerging opportunities early.

• Identify and deliver targeted savings. Lead the identification, business-case development, and execution of AI-enabled initiatives delivering meaningful, auditable cost savings and productivity gains across manufacturing, commercial, supply chain, and G&A functions.
• Own enterprise AI benefit tracking. Establish a rigorous measurement framework — baseline, target, realized savings, and run-rate impact — reported to the CEO and Board on a regular cadence.
• Manage the central AI investment budget. Hold accountability for a centrally managed AI investment budget, allocating capital across initiatives based on expected return, strategic fit, and risk. Establish a transparent chargeback or co-funding model with business units in Year 1 to align ownership with benefits.
• Drive adoption. Ensure AI tools and solutions are embedded into daily workflows, with appropriate training, change management, and performance accountability to realize planned benefits.

• Partner with IT on enterprise data governance. Coordinate with the IT organization to define and operationalize enterprise data governance standards, data quality requirements, master data management, access controls, and data lineage necessary to support AI initiatives.
• Co-own the AI-relevant data architecture. Collaborate with IT leadership on the data platform roadmap — including data lake / warehouse strategy, SAP data integration, and cloud AI services — ensuring AI requirements are reflected without duplicating IT’s infrastructure accountability.
• Champion responsible AI. Establish and enforce Ecovyst’s Responsible AI program covering data privacy, intellectual property protection, security, model risk management, bias mitigation, and regulatory compliance, aligned with IT, Legal, Compliance, and Internal Audit, and benchmarked against a recognized framework (NIST AI RMF, including the Generative AI Profile, or ISO/IEC 42001).
• Advance cybersecurity alignment. Work jointly with IT and the CISO function to ensure AI deployments meet enterprise cybersecurity standards and do not introduce new attack surfaces.

• Manage the Digitization & Optimization Manager. Provide direct supervision, coaching, and performance management to the Digitization & Optimization Manager, who will lead execution of specific AI and digitization initiatives.
• Develop an AI-fluent workforce. Partner with HR and business leaders to design and deliver AI literacy, reskilling, and upskilling programs for Ecovyst’s broader workforce.

• Chair the AI Steering Committee. Facilitate a cross-functional AI Steering Committee comprising senior leaders from Commercial, Operations, Finance, IT, HR, Legal, and Corporate Development to align priorities and unblock execution.
• Report to the CEO and Board. Provide regular updates to the CEO and Board of Directors on AI strategy execution, realized value, risks, and emerging opportunities.

• Bachelor’s degree in Engineering, Computer Science, Data Science, Operations Research, Industrial Engineering, Chemistry, Business, or a related discipline.
• Minimum of 10–15 years of progressive professional experience, including at least 5 years leading enterprise digital, analytics, or AI initiatives in an industrial, manufacturing, or comparable B2B environment. Candidates with deep generative-AI delivery experience post-2022 are strongly preferred; seasoned digital transformation leaders with credible, recent generative-AI exposure will also be considered.
• Demonstrated track record of delivering auditable, quantifiable productivity and cost-savings outcomes through AI, advanced analytics, or digital transformation programs.
• Experience building and leading high-performing teams in a matrixed, multi-site industrial organization.
• Working knowledge of enterprise data environments, including SAP ERP data structures, manufacturing execution systems, and common cloud AI platforms (e.g., Microsoft Azure AI, AWS, Google Cloud).
• Fluency with modern AI technologies — including machine learning, generative AI, large language models, computer vision, and process automation — and a clear, evidence-based point of view on where each creates value, grounded in firsthand delivery experience rather than secondhand familiarity.
• Experience partnering effectively with IT organizations on data governance, cybersecurity, and enterprise architecture without taking on IT operational accountability, and the personal credibility and judgment to navigate the natural friction such roles can create.

• Direct experience in an industrial or manufacturing environment, such as specialty or commodity chemicals, industrial services, refining, energy, industrial gases, building products, metals, mining, or comparable asset-intensive sectors.
• Experience deploying AI in commercial functions — pricing, demand forecasting, CRM analytics, or contract intelligence.
• Experience deploying AI in operations — predictive maintenance, process optimization, yield improvement, energy management, or HSE analytics.
• Experience supporting corporate development, M&A diligence, or post-merger integration analytics.