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.