Senior Data Analyst

relaytech.co
London
8 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Company mission

In the future, almost everything we consume will simply materialise on our doorsteps – what we call “e-commerce” today will simply be “commerce” tomorrow. But if we continue on today’s trajectory, the growth of e-commerce risks damaging the environment, alienating our communities, and straining the bottom-line for small businesses.

Relay is an e-commerce-native logistics network. We are built from the ground up for environmental, social, and economic sustainability. By building from the ground up we are able to entirely rethink both the middle and last mile enabling us to reduce the number of miles driven to deliver each parcel, lower carbon emissions, and lower costs, all while channelling funds to community members.

At the same time, we��re fixing the last broken aspect of e-commerce for consumers: delivery. As shoppers, we should have complete control over when and how we receive our purchases, and we should be able to return unwanted items as easily as we ordered them. That’s why whenever you buy from a merchant powered by Relay, you’ll be able to reschedule your delivery at any time. And if you don’t like what you ordered, at the tap of a button we’ll send someone to pick it up.

To orchestrate this complex ballet, Relay relies on a wide range of technologies, from advanced routing and planning to sophisticated user experiences that guide our team members on the ground.

About the role

As a highly operational business, we rely on data to guide everything we do. We are a small but impactful data team that works on everything from operations research to optimise thousands of parcel deliveries daily, to detailed business metrics that drive our expansion and investment decisions — and everything in between.

We’re currently hiring two Senior Data Analysts, each focused on a high-impact domain as we more than 10x our volume in the coming years: one will work on scaling our Middle Mile and Pitstop Networks, the other on optimising Last Mile Quality.

Both roles sit within our core data function and work closely with teams across routing, sortation, first mile, middle mile, last mile, marketplace, and our commercial functions. You’ll help define strategy, drive insights, and shape decisions at the heart of Relay’s network.

You’ll sit at the intersection of business and data, using your analytical skills to surface insights, shape strategies, and influence outcomes. From building robust performance dashboards to partnering with operators to improve network efficiency, you’ll play a key role in helping Relay scale sustainably and effectively.

These are high-impact roles with significant autonomy. You’ll be embedded in cross-functional teams, often leading analytical projects end to end – from problem definition to delivering recommendations that drive real-world outcomes.

What you'll do

  • Define key performance indicators and build dashboards that make operational performance transparent and actionable.

  • Proactively identify areas for improvement across the network and recommend strategic and operational initiatives.

  • Translate complex business problems into analytical questions – and analytical results into clear, actionable recommendations.

  • Collaborate with data scientists, data engineers, and software engineers to build data solutions that improve our network’s efficiency and resilience.

  • Design and maintain self-service data tools that empower teams to make informed decisions independently.

  • Take ownership of high-impact, cross-functional projects – driving them from concept to delivery and follow-through.

  • Spend time embedded in operational processes (e.g. at our sortation warehouse or delivery hubs) to identify opportunities and ensure solutions are grounded in reality.

What we're looking for

  • 6+ years of experience as a data analyst or in a similar role.

  • Excellent SQL skills and experience with BI/data visualisation tools.

  • Excellent analytical and problem-solving skills, with a proven ability to derive insight and drive change from data.

  • Effective communication skills – you can clearly explain technical results to both technical and non-technical audiences.

  • Highly collaborative and cross-functional experience, especially in fast-moving operational environments.

  • A commercial mindset – you care about impact, not just insight.

Nice to haves:

  • Experience working with geographic or operational data.

  • Experience in operationally intensive, fast-scaling businesses.

  • Familiarity with Tableau.

  • Comfort using Python (or similar) for data analysis and manipulation.

We're flexible on background — if you’ve got core analytical skills and a track record of driving impact, we’d love to hear from you.

What we offer

  • 25 days annual leave per year (plus bank holidays).

  • Equity package.

  • Bupa Global: Business Premier Health Plan - Comprehensive global health insurance with direct access to specialists, dental care, mental health support and more.

  • Contributory pension scheme.

  • Hybrid working.

  • Free membership of the gym in our co-working space in London.

  • Cycle-to-work scheme.

  • A culture of learning and growth, where you're encouraged to take ownership from day one.

  • Plenty of team socials and events - from pottery painting to life-size Monopoly and escape rooms.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.