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
• 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
• 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 informed operational or commercial decisions. You've taken models from notebook to production - writing maintainable code, building pipelines that run reliably, and debugging when they don't. You've contributed to methodology decisions and understand that a model isn't done when it trains well; it's done when it's running, monitored, and trusted.
• You have experience communicating with non-technical stakeholders. The squads that consume the forecasts need to trust them, and that trust comes from explaining what the models do, where they're reliable, and where they're not.
• You're comfortable using AI tools - LLMs, code assistants, and similar - to accelerate your workflow, from exploratory analysis to code generation, and you're curious about where these tools can augment the modelling process itself.
• This role suits someone who wants to see whether their models made a real difference to how the network operates - there is a direct feedback loop between your work and operational outcomes.
• Logistics or delivery network experience is a plus, but what matters more is the ability to learn a complex operational domain quickly and model it well.
• Generous equity, richer than 99% of European startups, with annual top-ups to share Relay's success.
• Private health & dental coverage, so comprehensive you'd need to be a partner at a Magic Circle law firm to match it.
• 25 days of holidays
• Enhanced parental leave.
• Located in Shoreditch, our office set-up enables the kind of in-person interactions that drive impact. We work 4 days on-site, with 1 day remote.
• Hardware of your choice.
• Extensive perks (gym subsidies, cycle-to-work, Friday office lunch, covered Uber home and dinner for late nights, and more).
• Aim with Precision: You define problems clearly and measure your impact meticulously.
• Play to Win: You chase bold bets, tackle the hard stuff, and view constraints as fuel, not friction.
• 1% Better Every Day: You believe that small, consistent improvements lead to exponential growth. You move quickly, deliver results, and learn from every experience.
• All In, All the Time: You show up and step up. You take ownership from start to finish and do what it takes to deliver when it counts.
• People-Powered Greatness: You invest in your teammates. You give and receive feedback with care and candour. You build trust through high standards and shared success.
• Grow the Whole Pie: You seek out win-win solutions for merchants, couriers, and our customers, because when they thrive, so do we.