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Lead Digital Transformation for Product Development Q.LIFE  Platform

The Q.LIFE Engine: A Work of Statistical Engineering: Quality Engineering: Vol 24 , No 2 - Get Access

Accelerating Speed to Market and Sustainability Using Digital Twins | Page 1

• Own and lead the end-to-end digital and AI transformation strategy for Product Development Q.LIFE  Platform, ensuring it continues to deliver differentiated value to Lubrizol Additives (LZA) and expand footprint to Lubrizol Engineered Materials (LEM) and Lubrizol Surface Sciences (LSS).
• Provide executive accountability for sustaining and evolving production ML models, ensuring reliability, performance, governance, and business continuity in LZA.
• Lead digital transformation in product & formulation development in partnership with IT, while maintaining strong alignment with scientific, engineering, and product development needs
• Establish standards, tooling, and best practices for model development, deployment, monitoring, retraining, and lifecycle management within the enterprise AI ecosystem.
• Strengthen and extend team capabilities in modern AI platform practices, including MLOps, automation, observability, and continuous improvement.
• Partner with the Computational Modeling pillar to integrate fundamentals-based modeling, bioinformatics, and cheminformatics capabilities, enabling next-generation Product Development Q.LIFE  Platform models for emerging business demands.
• Develop and continuously refine a technology roadmap for Product Development Q.LIFE  Platform; build and manage strategic partnerships with internal IT, external vendors/consultants, and universities to accelerate innovation.
• Act as a senior thought partner to leadership, helping the organization make informed, value-driven decisions about AI investments and priorities.

Lead Culture Change and Team Transformation

• Drive cultural transformation and change management by fostering a team environment that values curiosity, initiative, innovation, and continuous improvement.
• Establish project management best practices with expectations, accountability, and performance standards aligned with business impact, ownership, and execution excellence.
• Drive team growth and overall effectiveness through a capability-building approach that strengthens internal collaboration and engagement across functions, regions, business leaders, and external partners.

Talent & Resource Development:

• Partner with HR and global stakeholders to ensure alignment on compensation, career development, and succession planning.
• Support hiring strategies to bring in diverse skills, experiences, and perspectives, complementing and strengthening the existing team.
• Provide direct people leadership to align team goals, set priorities, and manage project prioritization across the portfolio to ensure effective execution and resource allocation.

Lead Statistics, Data Science, and Enterprise AI Enablement

• Shape the future of statistics in an AI era: lead the integration of statistical thinking to ensure robust, explainable, and scientifically grounded AI solutions
• Lead statistics and data science training efforts and coordinate enterprise-wide AI capability building, ensuring consistency, quality, and business relevance.
• Stay current with the rapidly evolving AI landscape, demonstrating the ability to quickly learn and adapt, filter signal from noise, and translate emerging AI capabilities into practical, scalable, and responsible enterprise use cases
• Partner with HR, IT, Operations, and business leaders to define role-based AI learning paths, balancing foundational literacy with deep technical expertise where needed.

Skills and Experience That Make a Difference

• Advanced degree (Ph.D., M.S.) in Engineering, Chemistry, Materials Science, Data Science, Computer Science, Statistics, or related fields.
• 12+ years of experience in chemical, materials, formulation, or closely related industries, with strong understanding of product development and AI/ML workflows
• Proven ability to lead major transformations, improve team performance, and promote accountability and results. Skilled at building strong digital teams and communities, encouraging experimentation, customer focus, and ongoing improvement.
• Experience building and leading cross-functional partnerships with IT, business units, external vendors, and academic institutions.
• Strong foundation in statistics, engineering, or scientific modeling, with the ability to challenge and validate AI/ML outputs,
• Excellent communication and influence skills, with the ability to operate effectively at executive, technical, and frontline levels.
• Proven people leadership of developing high-performing teams that embrace agility, AI insights, and collaborative problem solving
• Proven experience scaling, sustaining, and modernizing AI/ML systems in production.
• Strong technical depth across machine learning, MLOps, data platforms, and model lifecycle management, with the ability to engage credibly with senior technical and business stakeholders.