Senior Director, Computational Sciences & Digital Engineering

What matters most

• Strategy and Operating Model Definition

  You must define and execute a cohesive vision for the Digital Design & Material Innovation Center. This requires translating high-level business and technology goals into a concrete roadmap that integrates physics-based, data-driven, and hybrid AI/physics modeling into the enterprise R&D lifecycle.

• Enterprise-Scale Digital Transformation

  A core requirement is scaling digital tools and simulation capabilities to support 500-1,000+ users. You must establish standardized reference architectures, validation frameworks, and reusable model libraries that ensure consistency and quality across globally distributed teams.

• Cross-Functional Stakeholder Alignment

  Success depends on your ability to partner closely with the CIDO organization to align modeling tech stacks with enterprise infrastructure, cybersecurity, and data governance requirements, while simultaneously embedding modeling insights into product development and operations.

• Multidisciplinary Team Leadership

  You are responsible for building and leading a diverse, global team of experts in CFD, FEM, atomistic modeling, and AI/ML. You must foster a culture of scientific rigor and continuous improvement while managing resources across multiple international sites.

Likely challenges to think through

• Balancing Innovation with Governance

  The role requires driving rapid innovation while implementing strict portfolio governance and IP protection. Prepare to discuss how you manage the trade-off between allowing technical experimentation and maintaining the standardized, secure, and documented processes necessary for enterprise-level semiconductor R&D.

• Managing Global Cultural and Technical Integration

  Leading teams across Taiwan, Singapore, India, the U.S., and Korea presents significant coordination hurdles. Prepare to explain your approach to change management and how you ensure adoption of new digital tools across diverse R&D cultures that may be accustomed to legacy workflows.

• Translating Technical Complexity for Executive Leadership

  You must present complex computational strategies to C-suite and board-level audiences. The challenge is distilling technical nuances of multi-scale simulation and AI/ML into clear business outcomes, such as reduced innovation cycle time and improved product performance.

Topics to prepare

• Enterprise Modeling Governance and Lifecycle

  The role requires establishing portfolio governance, model validation frameworks, and documentation controls for 500-1,000+ users. Exercise: Draft a framework for a 'Model Lifecycle Management' policy that addresses version control, IP protection, and validation standards for simulation data, ensuring it aligns with cybersecurity requirements.

• Hybrid AI/Physics-Based Modeling Strategy

  You must define a roadmap for integrating physics-based simulations with data-driven AI/ML approaches. Exercise: Prepare a high-level presentation outlining how you would integrate a specific physics-based simulation (e.g., CFD or DFT) with machine learning to accelerate R&D cycles, focusing on business outcomes and technical risk reduction.

Skills to revise

• Executive Stakeholder Management

  The position requires presenting complex technical strategies to the C-suite and board. Exercise: Create a 3-slide executive summary that translates a technical initiative (e.g., scaling HPC infrastructure) into business value, focusing on innovation cycle time and product performance metrics.

• Multidisciplinary Team Leadership

  You will lead global teams of FEM/CFD, atomistic, and AI/ML engineers. Exercise: Develop a change management plan for transitioning a siloed R&D team toward a standardized, enterprise-wide modeling practice, specifically addressing cultural resistance and adoption of new digital tools.

• HPC and Cloud Infrastructure Strategy

  The role involves driving investment decisions for compute infrastructure and data platforms. Exercise: Review your experience with cloud-based simulation scaling and draft a set of criteria for evaluating whether to invest in on-premise HPC versus cloud-native solutions for semiconductor-grade modeling workloads.

• Synthesize an Enterprise Digital Transformation Case Study

  Prepare a narrative detailing your experience scaling simulation or AI tools to an organization of 500+ users. Focus on the change management, adoption programs, and cultural shifts required to move from siloed teams to an enterprise-wide modeling standard.

• Develop a Strategic Roadmap for Computational R&D

  Draft a high-level vision for a Digital Design & Material Innovation Center. Include how you would prioritize a portfolio of modeling initiatives (e.g., CFD, DFT, AI/ML) based on business value, technical risk, and innovation cycle time reduction.

• Prepare a Technical Governance Framework

  Outline your approach to establishing reference architectures, validation frameworks, and simulation data management. Be ready to discuss how you ensure model reuse, IP protection, and documentation controls across global, multidisciplinary teams.

• Formulate Cross-Functional Partnership Strategies

  Prepare examples of how you have collaborated with CIDO or CIO organizations to align modeling tech stacks with enterprise infrastructure, cybersecurity requirements, and scalable deployment frameworks.

• Draft Executive-Level Communication Points

  Create a summary of how you translate complex computational science strategies into business outcomes for C-suite and board-level audiences, focusing on semiconductor industry relevance and technology differentiation.

• Prepare Targeted Questions for Leadership

  Develop thoughtful questions regarding the current maturity of Entegris's simulation data management, the specific business priorities for the Digital Design center, and how the organization balances global collaboration between sites in Singapore, Taiwan, and the U.S.