Diagnose why a site underperforms on speed, cost, or throughput - then go fix it, on the floor, with the operations team
Build and apply AI across the operation - from getting robotics, vision systems, and automated sortation to actually deliver on the floor, to building your own AI tools and agents that cut manual work, surface problems faster, and make the team run better
Use Python and SQL - and AI tools that speed up diagnosis - to find the problem in the data, size the opportunity, and track whether your change held
Own improvement projects end-to-end: scope, build, implement, measure - across multiple markets
Requirements:
Bachelor's degree, preferably in Business, Engineering (Industrial, Mechanical, Computer Science, Operations Research), or a quantitative degree
At least 2 years of hands-on experience (inclusive of structured internships) within operations, manufacturing, or supply chain environments, with a focus on executing workflow improvements.
Demonstrated experience leading academic projects or participated in hackathons/challenges will be advantageous.
Proficient in SQL & Python
Hands-on exposure to AI - whether physical (robotics, automation, computer vision) or software (ML, LLMs, agents, automation tooling) - is a strong plus. This role lets you build on both sides
Willingness to Investigate On-Site: Committed to conducting direct, on-site diagnostics at sortation centres rather than relying solely on dashboards fully prepared to travel and work alongside ground teams.
Curious about the why, sharp on detail, and energised by making a physical system run better
