THE TEAM• ~160 people, around half in engineering, product and data
• 45+ advanced degrees across computer science, mathematics and operations research
• Thousands of data points captured, calculated, analysed and predicted for every single parcel we handle
• An intellectually vibrant culture of first‑principles thinking, tight feedback loops and relentless experimentation
Nobody has solved quality in delivery. The industry treats lost and undelivered parcels the way it treats weather: absorbed, apologised for, credited back. Retailers have learned to expect it. Carriers report on-time rates and go quiet on everything else, because when a parcel goes missing between a van and a sorting centre, nobody can say exactly where it happened or who should fix it.
We’ve spent the last year building the systems and infrastructure to make that answerable: capturing what actually happens at every handover and attributing loss to a specific point in the journey, with one team accountable for it. Thousands of data points per parcel give us a foundation most of this industry simply doesn’t have.
That turns delivery quality from a reporting exercise into a modelling problem, and creates a rare applied AI opportunity in a huge physical industry. We treat every failed delivery as another signal: something to understand, model and feed back into a learning loop. We run ML and LLM inference daily, with the data density and engineering depth to go after the problem properly.
This role is where those models meet the customer, and where insight becomes action.
Get this right and the relationship changes. Instead of explaining a miss after the fact, you’re showing a client where their parcels are being lost, what we’re doing to fix it and what we expect that intervention to be worth, before they’ve had to ask. They can plan around us. They stop chasing and start relying on us to tell them first. That’s what turns a supplier into a strategic partner.
You’ll own two pictures and the connection between them: internally, a single view of what the network is doing to improve quality and in what order; externally, the performance story each strategic account sees. Today those views live in different places. Your job is to join them.
You’ll lead a small team, with data, engineering, product and our operational pillars around you. You won’t be building the underlying capture and attribution systems. Our engineering and data teams own that, and you inherit them. Which means you start from numbers you can trust, and can focus on the harder question: what should we do about them?