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UiPath is seeking a Forward Deployed Scientist I to work on applied AI and machine learning problems in the Supply Chain & Retail business unit. You will own customer engagements end-to-end, working directly with major retailers and manufacturers on high-stakes problems including demand forecasting, price and promotion optimization, and supply-chain decisioning.
In this role, you will apply cutting-edge machine learning, forecasting, and optimization techniques—including agentic AI systems—to real-world commercial problems that lack textbook solutions. You'll frame ambiguous problems, build solutions, and drive them to measurable commercial impact alongside customer teams. Your work will directly inform product direction, ensuring each engagement improves core capabilities for future customers.
You'll operate with genuine autonomy across diverse stakeholders, from data scientists to customer executives, making judgment calls to ship solutions. You'll stay at the frontier of applied AI and bring emerging techniques back to the team.
Required qualifications include a strong track record applying ML and data science to real-world commercial problems, ideally with depth in forecasting, optimization, supply-chain, or pricing. You must demonstrate ownership instinct—taking ambiguous problems to completion rather than waiting for direction. Fluent programming in Python and SQL is essential, along with the ability to learn whatever a problem demands. Excellent communication skills are critical: you can guide customer executives from confusion to confidence and explain complex models to non-technical audiences. You should be comfortable with ambiguity, direct customer contact, and the pace of a shipping team.
Bonus experience includes agentic or LLM-based systems, and building ML in production environments. The company values builders—smart, switched-on people who want to tackle genuinely hard real-world challenges. Passion and diverse backgrounds are encouraged; the company explicitly welcomes applications from candidates who may not tick every box but believe they'd excel in the role.
