Industry Architect - R&D

Build executive presence and credibility with R&D leaders, with the mindset to drive strategic, first-of-its-kind R&D engagements. Lead R&D opportunities from pre-sales through delivery, including technical scoping, use case qualification, customer readiness, data and compute assessment, solution feasibility, business value definition, staffing models, success criteria, effort estimates, and phased delivery plans. Bridge business objectives, scientific requirements, and technical implementation teams to ensure successful end-to-end solution delivery. Work side-by-side with customer scientists, researchers, computational teams, engineers, executives, and UX researchers to articulate specific R&D outcomes and roadmaps, align technical delivery with business outcomes, and establish trusted advisor relationships. Lead design and delivery of MVPs, proof-of-concepts, and production-ready solutions across Discovery workflows, agents, integrations, simulations, and data pipelines. Own technical workstreams across the engagement lifecycle: qualification and planning, discovery and design, implementation and build, and post-go-live optimization Evaluate emerging AI capabilities and provide scientific and technical oversight throughout the engagement, ensuring solutions are scientifically credible, technically feasible, aligned to customer goals, and applicable to scientific and engineering workflows. Develop reusable patterns, reference architectures, delivery playbooks, technical assets, and leading practices that help scale R&D patterns across customers, industries, and regions. Scientific Credibility: Earns deep trust from research leaders by demonstrating deep understanding of R&D problems and research workflows. Technical Credibility: Earns trust with R&D IT and computational teams by demonstrating proven understanding of HPC, simulation, scientific workflows, data pipelines, security, and production operations. Problem Framing and Solutioning: Shapes ambiguous R&D and science problems into clear hypotheses, workflows, architectures, delivery plans, risks, and decision points. R&D Product Lifecycle Management: Establishes and manages high-quality product roadmaps and product backlogs for delivery teams, connecting scientific priorities to engineering execution. R&D Outcome Ownership and Accountability: Establishes, monitors, and presents R&D outcomes to external and internal stakeholders throughout pre-sales and delivery, including impact on cycle time, experiment throughput, decision quality, cost-to-discover, or speed of scientific insight. Continuous Learning and Cross-Industry Adaptability: Stays curious about how R&D operates across industries, learns beyond their primary domain, and is willing to apply their skills across adjacent R&D-intensive sectors when customer needs, scientific opportunities, or business priorities require it. Extensively experienced professional in science, engineering, technology consulting, product management, data science, machine learning, AI, or a related field. Direct experience in materials science or industrial research and development, including chemistry, polymers, advanced materials, formulation science, computational materials, specialty chemicals, manufacturing research and development, energy materials, or related materials-focused domains. Demonstrated understanding of scientific methods, experimentation, data-informed decision making, and research and development workflows. Experience applying AI, machine learning, generative AI, data science, simulation, or advanced analytics to business or scientific challenges. Proven ability to collaborate effectively with technical specialists and business stakeholders. Effective communication, storytelling, and stakeholder management skills. Demonstrated ability to influence across teams and drive organizational outcomes. Analytical and problem-solving skills in evolving and complex environments. Doctor of Philosophy, Master of Science, or equivalent experience in Chemistry, Biology, Physics, Engineering, or a related discipline. Demonstrated understanding of data science and machine learning, including algorithms, exploratory data analysis, model development, and model evaluation. Experience with product management, Agile delivery practices, and backlog management. Experience working across large organizations and collaborating with engineering, research, product, and sales teams. Track record of leading innovative initiatives and new solution development programs. Experience using GitHub Copilot, Claude Code, Codex, or similar tools.