Sr Data Scientist

📋 Description

• Own sourcing research initiatives end to end - from framing the problem on real spend data, through
• Build what does not exist yet. Most of these problems have no baseline to beat and no off-the-shelf
• Turn the network into a training set. Define the datasets, labels and benchmarks that make sourcing
• Design and run bold experiments with a hacker mindset - fast prototypes, honest baselines and
• Build on our T-LLM foundations wherever documents carry the signal - RFPs, specs, quotes, contracts
• Ship with deployment in mind: inference cost, latency, robustness, and what happens when a

🎯 Requirements

• 5+ years in applied ML, data science, ML engineering or quantitative research, with models or
• Strong Python and real comfort with messy, large-scale data - including SQL and the unglamorous
• Depth in at least one modelling discipline, curiosity about the rest. Deep learning, recommendation
• Scientific rigour. Strong experiment design, healthy scepticism about your own metrics, and real
• Ownership and curiosity. You are comfortable in a greenfield, ambiguous problem space, and you will
• Interest in procurement, supply chains or market design is welcome but not required - we will teach

🎁 Benefits

• Greenfield problems in a mature product: Modern ML has barely been applied to sourcing, inside a
• Data nobody else has: $10 trillion of transacted spend, 10M+ buyers and suppliers, and the
• We train our own models: Proprietary T-LLM architectures, designed and trained in-house - not a
• Real ownership, short path to customers: You frame the problem, choose the method, and see it
• Global impact: Technology used every day by companies around the world.
• Experiment-driven culture: Pragmatic delivery, and quarterly recognition for standout research