Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins. Why join Coupa? Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend. Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence. Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other. Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa. About the Role Sourcing is where the money is actually decided. We are expanding our Data Capture Research team in Prague with a Senior Data Scientist to work on Sourcing. Which suppliers get invited. How the event is structured. How bids that differ in price, lead time, quality, risk and carbon get compared at all. What a fair price even is. When to award, to whom, and how to split the award across suppliers. For a researcher this is unusually open ground - not one model family, but several, on the same data: Recommendation and retrieval. Supplier discovery: matching demand to the right suppliers across a 10M-node network. Forecasting and should-cost modelling. What this category, in this region, at this volume, should cost right now. Game theory and mechanism design. Auction formats, bidding behaviour, incentives, and competitive dynamics between real counterparties. Combinatorial optimisation. Award allocation under volume, capacity and multi-sourcing constraints. Multimodal document understanding. RFPs, specs, quotes and contracts carry the actual requirements - and our T-LLM foundations already give us a head start there. Part of the work is choosing the right instrument for each - neural networks, gradient boosting, optimisation, bandits, mechanism design - instead of forcing one. Very little of this has been built with modern ML yet. That is the point of the role: real greenfield problems, a dataset nobody else has, and a product that ships to companies whose margins depend on getting these decisions right. You will work in a small, senior team of researchers and engineers - the group that built Rossum's production models from scratch - with direct access to Product and the AI Platform team. Ideas that work do not sit on paper; they roll into systems used at scale.