Relay Technologies on cord

Relay Technologies

Staff Data Scientist

External position
London, UK
Hiring internally for Relay Technologies?
Logistics · E-Commerce
Re-wiring delivery infrastructure, enabling retailers to supercharge their e-commerce growth
Posted
a month ago
Checked
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6 hours ago

Skills & Experience

Job roles: Data Scientist
Experience level: Senior, Lead
Core skills considered: Python, SQL, Data Analysis, Data Modeling, Machine Learning
Other skills considered: Deep Learning, Data Pipelines, Data Warehousing, Agile

Logistics

Base salary: Undisclosed
Some companies on cord are unable to disclose salaries publicly due to internal company policies. Message the company for salary information.
Employment type: Permanent
Remote working: Hybrid (up to 1 remote day p/w)
Visa sponsorship: Not available

Job Description

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, 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. It means owning the forecast of inbound international volume, one of the hardest prediction problems we have. And it means owning the models that predict each parcel's size and weight, which turn a parcel count into the physical volume that actually has to be sorted and loaded.

This is a hands-on role. You will spend most of your time building, not managing. You set the direction for how Demand Forecasting models, evaluates and ships its work, and you raise the technical bar across the area, but you do it as the most senior individual contributor in the room, on the tools. You'll work alongside a Senior Data Scientist who owns the domestic volume forecasts, an ML Engineer who keeps the models running reliably in production, and an Analyst who owns forecast accuracy and data quality. People leadership, strategy and cross-squad priorities sit with the Data Science Manager who leads the squad; you own the science.

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.


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-leverage 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, with what guarantees, and how error is attributed when a number turns out wrong.
  • 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.
  • Own production quality across the estate, working with the ML Engineer so that models are monitored, drift is caught early, and accuracy problems are traced to the right cause rather than re-tuned blindly.
  • Work with Finance, who extend the operational forecasts into longer-range financial projections, to keep the handoff between operational and financial models reliable.
  • Quantify the impact of model error on cost per parcel, and use it to decide where the area invests effort.

Who Will Thrive in This Role?

You have been the technical anchor on a modelling team before. You've owned the hardest problems, set the standards others worked to, and been the person the team turned to when an approach needed a call. You did it hands-on, as a senior IC, not by moving into management, and that's the path you want to keep on.

You think in interconnected systems. A demand forecast isn't just a number; it drives how many shifts are opened, how many vans are dispatched and how routes are planned, and it feeds a downstream planning decision and the network's expansion models. You care about how your models connect to the models around them, and you design their interfaces deliberately.

A deep track record of building and delivering models from ambiguous starting points. You understand the problem, build something useful, validate it against real operations, and iterate. You evaluate models well beyond standard offline metrics, connecting outputs to downstream applications and business KPIs and measuring how improvements translate into operational impact.

Strong Python and SQL, and depth across the full modelling lifecycle - from data extraction and feature engineering through training, validation and production deployment. You've worked with time-series forecasting across classical statistical approaches, gradient boosting and deep learning, and you understand the trade-offs well enough to make and defend the choice for a given problem. You'll be supported by a dedicated ML Engineer, but you set the standard for how the area's models are built.

At least 8 years in a data science or quantitative modelling role, with clear examples of models you built that informed operational or commercial decisions, and of methodology or technical direction you set for a team. You know a model isn't done when it trains well; it's done when it's running, monitored, and trusted.

You communicate with non-technical stakeholders with authority. 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, without hiding behind the maths.

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, and as the Staff DS you own the most consequential end of it.

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.

Staff Data Scientist at Relay Technologies
Position posted a month ago

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Starling Bank on cord

Starling Bank

Full Stack Engineer (ML Ops)

London, UK
FinTech · Banking · Finance
A better bank for everyone
Active
over 6 months ago
Responds to
0% of requests
Responds in
1h 55m
Requests
7 pending

Skills & Experience

Job roles: Full Stack
Experience level: Mid, Senior
Core skills considered: Java, React, Redux, AWS, JavaScript

Logistics

Base salary: £70K - £110K
Employment type: Permanent
Remote working: Hybrid (up to 2 remote days p/w)
Visa sponsorship: Not available

Job Description

Starling is the UK’s first and leading digital bank on a mission to fix banking! Our vision is fast technology, fair service, and honest values. All at the tap of a phone, all the time.

Starling is the UK’s first and leading digital bank on a mission to fix banking! We built a new kind of bank because we knew technology had the power to help people save, spend and manage their money in a new and transformative way.

We’re a fully licensed UK bank with the culture and spirit of a fast-moving, disruptive tech company. We’re a bank, but better: fairer, easier to use and designed to demystify money for everyone. We employ more than 3,000 people across our London, Southampton, Cardiff and Manchester offices.

Our technologists are at the very heart of Starling and enjoy working in a fast-paced environment that is all about building things, creating new stuff, and disruptive technology that keeps us on the cutting edge of fintech. We operate a flat structure to empower you to make decisions regardless of what your primary responsibilities may be, innovation and collaboration will be at the core of everything you do. Help is never far away in our open culture, you will find support in your team and from across the business, we are in this together!

The way to thrive and shine within Starling is to be a self-driven individual and be able to take full ownership of everything around you: From building things, designing, discovering, to sharing knowledge with your colleagues and making sure all processes are efficient and productive to deliver the best possible results for our customers. Our purpose is underpinned by five Starling values: Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness.

Hybrid Working

We have a Hybrid approach to working here at Starling - our preference is that you're located within a commutable distance of one of our offices so that we're able to interact and collaborate in person. We don't like to mandate how much you visit the office and work from home, that's to be agreed upon between you and your manager.

Our Data Environment

Our Data teams are excited about the value of data within the business, powers our product decisions to improve things for our customers and enhance effective and agile decision making, regardless of what their primary tech stack may be. Hear from the team in our latest blogs or our case studies with Women in Tech.

We are looking for talented data professionals at all levels to join the team. We value people being engaged and caring about customers, caring about the code they write and the contribution they make to Starling. People with a broad ability to apply themselves to a multitude of problems and challenges, who can work across teams do great things here at Starling, to continue changing banking for good.

Requirements

We have built our entire banking platform in house and mostly in Java. We are looking for people who want to work on building the tooling that is used by our engineers on a daily basis.

We are looking for people who are truly full stack, and are as comfortable polishing their javascript front end as they are debugging the innards of their java applications database interactions, or standing up infrastructure with terraform. We are looking for people who can:

  • Design REST apis.
  • Code backend services, ideally using Java, or another other server side compiled language.
  • Develop modern front ends, ideally using React and Redux.
  • Get their code into the cloud and support it there, ideally on AWS.
  • Believe in clean coding, simple solutions, automated testing and continuous deployment.
  • Like to take ownership of a feature from the original idea through to live.
  • Think (like us) that a small number of empowered developers is the right way to deliver software.

Company Benefits

  • 33 days holiday (including flexible bank holidays)
  • An extra day’s holiday for your birthday
  • 16 hours paid volunteering time a year
  • Part-time and/or flexible hours available for most roles
  • Salary sacrifice, company enhanced pension scheme
  • Life insurance at 4x your salary
  • Hybrid/remote working
  • Private Medical Insurance with VitalityHealth including mental health support and cancer care. Partner benefits include discounts with Waitrose, Mr&Mrs Smith and Peloton
  • Generous family-friendly policies
  • Varied social groups set up and run by our employees
  • Perkbox membership giving access to retail discounts, a wellness platform for physical and mental health, and weekly free and boosted perks
  • Access to initiatives like Cycle to Work, Salary Sacrificed Gym partnerships and Electric Vehicle (EV) leasing

Full details are available on our careers site

Interview Process

Interviewing is a two way process and we want you to have the time and opportunity to get to know us, as much as we are getting to know you! Our interviews are conversational and we want to get the best from you, so come with questions and be curious. In general you can expect the below, following a chat with one of our Talent Team:

  • Stage 1 - 30 mins with one of the team
  • Stage 2 - Take home challenge
  • Stage 3 - 90 mins technical interview with two team members
  • Stage 3 - 45 min final with an executive and a member of the people team
Alex Yeates on cord
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