Forward Deployed Scientist II

This is one of the most exciting problems in applied AI right now: take the cutting edge of machine learning, optimisation, and agentic systems, and make it work inside real businesses — not in a lab, but on live customer problems where the answer has to hold up commercially. As a Forward Deployed Scientist II, in our Supply Chain & Retail business, you’ll work directly with some of the world’s biggest retailers and manufacturers on hard, high-stakes problems: demand forecasting, price and promotion optimisation, and supply-chain decisioning. You’ll own the science end-to-end — from understanding a customer’s messy reality to shipping something that changes how they operate. And you won’t just deliver. What you learn on the front line feeds directly back into the products we build, so you’re shaping the capability the whole business runs on — not just solving one problem at a time.

Responsibilities

Own customer engagements end-to-end — frame the problem, build the solution, and drive it to real commercial impact, working directly alongside customer teams; Apply and adapt frontier ML, forecasting, and optimisation techniques — and increasingly agentic AI — to problems that don’t have a textbook answer; Turn what works on the ground into product direction, so each engagement makes our core capability better for the next customer; Operate with genuine autonomy across a wide range of stakeholders — from data scientists to customer executives — and make the judgment calls that get things shipped; Stay at the edge of what’s possible in applied AI, and bring it back to the team

Requirements

Strong track record applying ML / data science to real-world commercial problems; The instinct to own outcomes, not tasks — you take an ambiguous problem and drive it to a result rather than waiting to be told the next step; Fluent programming (Python and SQL) and the ability to pick up whatever a problem demands; Excellent communication — you can take a customer executive from confusion to confidence, and explain a complex model to a non-technical room; Comfort with ambiguity, direct customer contact, and the pace of a team that ships

Nice to have

Depth in forecasting, optimisation, or supply-chain / pricing; Experience with agentic / LLM-based systems, or building ML in production