Staff Data Scientist

About the role

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. The Network squad builds and maintains the forecasting engine that powers all of it, and Demand Forecasting is its core: one forecast of what will be available to sort, at outcode granularity, from D0 out to D30. As a Staff Data Scientist, you are the technical anchor for Demand Forecasting. You own the hardest and most ambiguous parts of the forecast, and you set the methodology and validation standards the rest of the area works to. That means owning the single integrated forecast of what volume the network will have to move, by area and out to thirty days, which the demand-management layer then turns into the operational plan the sort centres and transport teams run on. It means owning the model-driven end of that forecast, where the horizon runs past any live tracking data and expected parcels have to be generated from models rather than observed.

What you'll do

• Own the integrated forecast end to end. Blend live tracking signals in the near term with model-generated parcels further out into a single view, by area, from today to thirty days ahead. This is the view the demand-management layer turns into the plan that Sortation, Middle Mile and Last Mile actually run on.
• Build the hardest models in the area. The model-generated long-horizon forecast, the inbound-international volume forecast, and the parcel size and weight models that make the forecast a measure of physical volume rather than just a count. These are the ambiguous, high- problems, and they are yours.
• Set the methodology and validation standards for Demand Forecasting. Define how models are evaluated, how accuracy is measured at each horizon (a forecast made thirty days out shouldn't be held to the standard of one made two days out), and what "good" looks like across the area's models.
• Raise the technical bar. Review approaches, make the model-choice and build-vs-buy calls, and mentor the Senior Data Scientist and Analyst alongside you, without taking on their line management.
• Define the forecast's interfaces. Decide what Demand Forecasting hands to the demand-management layer, to Routing, and to the network-planning function: at what granularity, in what aggregation, and with what uncertainty attached.
• Partner with the ML Engineer to ensure the Demand Forecasting models are production-ready, scalable, and resilient in the face of late-breaking data and shifting sort centre constraints.
• Translate ambiguous operational questions into well-defined forecasting problems, collaborating with the Analyst on data quality and the business teams on the implications of forecast error.
• Drive clarity on priorities between the domestic and international forecasting workstreams, ensuring the methodology serves both regimes without losing coherence.

Requirements

• You hold a PhD or Master's in a quantitative field such as computer science, mathematics, or operations research.
• You have extensive experience building and deploying production forecasting models for time series with complex seasonality and multiple horizons.
• You are comfortable working with sparse, noisy, and non-stationary data where observed signals are only a partial view of reality.
• You have a strong track record of rigorous validation, including backtesting strategies that respect temporal structure and realistic evaluation frameworks.
• You are fluent in Python and modern data science tooling, with experience in scalable data pipelines and model serving.
• You are comfortable communicating uncertainty and model limitations to non-technical stakeholders and framing trade-offs clearly.
• You have experience collaborating with cross-functional partners to align forecasting with operational constraints and business impact.
• You are comfortable making decisions with incomplete information and documenting assumptions rigorously so that others can build on your work.

Nice to have

• Experience with large-scale logistics or transportation networks.
• Familiarity with inventory theory, queueing, or network optimisation.

Practical notes

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 and report into the centralised data team. Demand Forecasting is growing, and as its Staff DS you will have the deciding voice on its technical direction and the modelling approaches it adopts. 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 and report into the centralised data team. Demand Forecasting is growing, and as its Staff DS you will have the deciding voice on its technical direction and the modelling approaches it adopts. 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 and report into the centralised data team. Demand Forecasting is growing, and as its Staff DS you will have the deciding voice on its technical direction and the modelling approaches it adopts. 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 and report into the centralised data team. Demand Forecasting is growing, and as its Staff DS you will have the deciding voice on its technical direction and the modelling approaches it adopts. 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 and report into the centralised data team. Demand Forecasting is growing, and as its Staff DS you will have the deciding voice on its technical direction and the modelling approaches it adopts. 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

Location: UK

Hybrid.

Engagement: Full-time. The role sits within the Network squad in our centralised data team, and you will report into this centralised data team. Demand Forecasting is growing, and as its Staff DS you will have the deciding voice on its technical direction and the modelling approaches it adopts. 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 and report into the centralised data team. Demand Forecasting is growing, and as its Staff DS you will have the deciding voice on its technical direction and the modelling approaches it adopts. 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 and report into the centralised data team. Demand Forecasting is growing, and as its Staff DS you will have the deciding voice on its technical direction and the modelling approaches it adopts. Relay operates a repeated statement regarding the structure of the data team and the reporting lines, ensuring that the candidate understands where this role sits and how it will influence technical strategy. The role is full time and based in London with a hybrid working model.