Relay operates an end-to-end parcel delivery platform for e-commerce, using urban mini-depot pitstops, local couriers and e-bikes, route-matching and returns consolidation, with computer vision-enhanced proof-of-delivery.
Relay is fundamentally reshaping how goods move in an online era. Backed by Europe’s largest-ever logistics Series A ($35M), led by deep-tech investors Plural (whose portfolio spans fusion energy and space exploration), Relay is scaling faster than 99.98% of venture-backed startups. We're assembling the most talent-dense team the logistics industry has ever seen Relay’s Mission is to free commerce from friction. Today, high delivery costs act as a hidden tax on e-commerce, quietly shaping what can be sold online and limiting who can participate. We envision a world where more goods move more freely between more people, making the online shopping experience seamless and accessible to everyone. THE TEAM • ~110 people, more than 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 About the role Relay's network runs on forecasts. Every shift released in sortation, every middle-mile van dispatched, every last-mile route planned, every expansion decision made - all downstream of models that predict how parcels move through our system. When those models are right, the network runs efficiently and cost per parcel drops. When they drift, the cost compounds across every stage of the operation. The Network squad builds and maintains the forecasting engine that powers all of it. As a Senior Data Scientist in the Network squad, you will lead a core domain within that engine, working alongside other Data Scientists and Analysts who each bring different expertise. The scope spans demand forecasting, expansion modelling, parcel intelligence, and sortation predictions - and the specific domain you take on will depend on your strengths and what the squad needs most. What's common across all of it: you will build and maintain models that directly drive operational decisions for multiple teams across Relay, every day. This means different things depending on the domain. It might mean building the expansion models that determine where Relay grows next: which outcodes to enter, how volumes ramp in new areas, and how the pitstop network should evolve. It might mean working on the dimensions model that predicts parcel weights and sizes before they arrive, feeding route planning and vehicle loading decisions. It might mean extending the demand forecast horizon from 7 days toward 30, giving downstream teams more lead time to plan. Or it might mean improving the predictions that determine what volume is available to sort on a given night, and how that volume should be allocated across sort centres. Relay operates a centralised data team of around 30 Data Engineers, Analysts, and Data Scientists, with specialists embedded into squads across the business. You will sit in the Network squad alongside other Data Scientists and Analysts, reporting into the centralised data team. The squad is growing, and you will have significant influence over its technical direction and the modelling approaches it adopts. You will be supported by Analysts who build the monitoring and reporting layer, and a squad lead who sets strategic direction. What You'll Do - Build and maintain forecasting models within your domain - from initial exploration through to validation and production deployment - Learn the operational processes your models serve, supported by the squad and the teams who use the forecasts, and identify where the current approach falls short - Monitor how your models perform in production, investigate when accuracy drops, and work with the squad to improve them - Contribute to methodology decisions and validation approaches, working with other Data Scientists in the squad to improve the forecasting engine over time - Translate problems from consuming squads into data science problems - Sortation, Middle Mile, Last Mile, Routing, and Commercial each depend on Network's forecasts - Work with Finance, who extend the operational forecasts into longer-range financial projections, to ensure the handoff between operational and financial models is reliable - Quantify the impact of model errors on cost per parcel, helping the squad and stakeholders prioritise where to invest effort - Influence the squad's technical direction and modelling approaches as the team grows Who Will Thrive in This Role? - Experience thinking about interconnected systems - understanding that a demand forecast isn't just a number, but flows through shift release, van dispatch, route planning, and courier allocation. You're interested in how your models connect to the models around them. - A track record of building and delivering models. You've worked from ambiguous starting points before - understanding the problem, building something useful, validating it against real operations, and iterating. You evaluate models beyond standard offline metrics - connecting outputs to downstream applications and business KPIs, and measuring how improvements translate into operational impact. The squad works collaboratively to define priorities and scope, and you'll have support from the squad lead and your peers as you ramp up - Strong Python and SQL, and comfort working across the modelling lifecycle - from data extraction and feature engineering through to model training, validation, and production deployment. You've worked with time-series forecasting methods - whether classical statistical approaches, gradient boosting, deep learning, or a combination - and you understand the trade-offs between them. Experience with data engineering is useful, and you'll be supported by a dedicated engineer in the squad. - You have at least 5 years of experience in a data science or quantitative modelling role, with examples of models you built that info