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Coupa is expanding its Data Capture Research team in Prague to hire a Senior Data Scientist focused on Sourcing—a greenfield area where modern ML has barely been applied to procurement and supply chain optimization.
About the Role
You will own sourcing research initiatives end-to-end, from problem framing on real spend data through experiments to production deployment. Sourcing at Coupa involves multiple modeling disciplines: recommendation and retrieval for supplier discovery across a 10M-node network; forecasting and should-cost modeling to predict category pricing; game theory and mechanism design for auction formats and bidding behavior; combinatorial optimization for award allocation under constraints; and multimodal document understanding of RFPs, specs, quotes, and contracts using proprietary T-LLM foundations.
Key responsibilities include:
- Own sourcing research initiatives end-to-end, from problem framing through production deployment
- Build novel solutions where no baseline or off-the-shelf answer exists; define what the first version looks like
- Turn Coupa's network into a training set by defining datasets, labels, and benchmarks; derive supervision from historical events and outcomes
- Design and run bold experiments with fast prototypes, honest baselines, and offline evaluation that predicts online behavior
- Leverage proprietary T-LLM foundations for document-based signal extraction
- Ship with deployment in mind: optimize for inference cost, latency, robustness, and real-world failure modes
- Collaborate across Sourcing product, data teams, and the AI Platform team; document decisions and mentor peers
You will work in a small, senior team of researchers and engineers (the group that built Rossum's production models) with direct access to Product and the AI Platform team. Ideas that work ship to scale; there is no research-to-product handoff.
Why This Matters
Coupa's platform processes $10 trillion in transacted spend across 10M+ buyers and suppliers. Sourcing decisions—which suppliers to invite, how to structure events, how to compare bids on price, lead time, quality, risk, and carbon, and how to award—directly impact operating margins for enterprise customers worldwide. This is greenfield work with a dataset nobody else has.
Requirements
- 10+ years in applied ML, data science, ML engineering, or quantitative research, with models or decision systems you took into production
- Technical leadership: you have set direction for a research area, scoped problems, mentored people solving them, and stayed accountable for shipping models
- Strong Python and comfort with messy, large-scale data; proficiency in SQL and the unglamorous work of making datasets trustworthy
- Depth in at least one modeling discipline with curiosity about others: deep learning, recommendation and ranking, forecasting and time series, optimization and operations research, causal inference and econometrics, RL and bandits, market and mechanism design, or LLM-based systems
- Scientific rigor: strong experiment design, healthy skepticism about metrics, and real care about leakage, baselines, and evaluation that survives production contact
- Ownership and curiosity in greenfield, ambiguous problem spaces; comfort talking to product and procurement experts to find real value
- Interest in procurement, supply chains, or market design is welcome but not required; domain knowledge can be taught
