Job Description

Overview

Microsoft is helping define a new era of scientific and industrial innovation: one where agentic AI, cloud-scale compute, data, simulation, and human expertise come together to accelerate breakthroughs that once took months or years. As customers look to transform how they discover new materials, medicines, products, processes, and research insights, they need more than a platform — they need deeply technical partners who can turn ambition into working solutions. This role has three distinct dimensions: shaping customer outcomes, leading technical delivery, and scaling R&D motions.

We are seeking an Industry Architect, executive presence, and leadership ability to shape complex R&D opportunities. You will work directly with R&D leaders, scientific researchers, account teams, industry leaders, and Microsoft product engineering to identify high-value R&D scenarios. The Industry Architect serves as the strategic bridge between scientific innovation, business outcomes, and Microsoft’s AI technologies, shaping high-value opportunities from pre-sales through delivery and leading first-of-its-kind MVPs and cutting-edge production deployments.

This is a role for builders who thrive in ambiguity, move comfortably from high level customer needs to whiteboard to code, and are energized by solving complex customer problems in the real world. You will help customers reimagine R&D workflows through Microsoft AI while also shaping the repeatable patterns, product feedback, and delivery motions that scale this frontier AI business across industries. If you are excited by the opportunity to co-engineer with customers, unlock new scientific and industrial possibilities, and help establish one of Microsoft’s most strategic AI growth motions, this role is for you.

Responsibilities

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.
Partner with account teams, Industry Advisors, Customer Success, Product Engineering, and Services teams to shape R&D engagements from early opportunity through implementation, drive cross-functional alignment across Microsoft.
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.
Translate customer R&D, scientific, engineering, or advanced computing challenges into product backlogs, epics, user stories, tasks, research objectives, success metrics, scientific workflows, and target-state AI architectures on Microsoft Discovery and Azure.
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. Capture product feedback and field learnings to influence the Microsoft Discovery roadmap, engineering priorities, and repeatable industry motions.

Success in this role looks like

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.
Field learnings improve Microsoft Discovery product direction and delivery playbooks.
Customers view Microsoft as a trusted technical partner for frontier AI and R&D transformation.

Qualifications

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.

Preferred Qualifications

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.
Familiarity with Microsoft Azure cloud services (for example, Foundry and AI Search), agentic AI architectures, knowledge graphs, microservices, containers, and scientific high-performance computing.
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.

Solution Architecture IC6 - The typical base pay range for this role across Germany is € 117,400.00 - € 252,500.00 per year. Certain roles may be eligible for benefits and other compensation.