Lead Data Analyst - Network

Lead Data Analyst - Network

Full-Time 60000 - 80000 £ / year (est.) Home office (partial)
Relay

At a Glance

  • Tasks: Oversee forecasting systems, track model performance, and ensure data accuracy.
  • Company: Relay, a fast-growing logistics startup backed by major investors.
  • Benefits: Competitive salary, diverse team, and opportunities for career growth.
  • Other info: Join a vibrant team focused on innovation and continuous improvement.
  • Why this job: Make a real impact on e-commerce logistics and improve online shopping experiences.
  • Qualifications: 5+ years as a data analyst, fluent in SQL, and strong analytical skills.

The predicted salary is between 60000 - 80000 £ per year.

Relay is fundamentally reshaping how goods move in an online era. Backed by Europe’s largest-ever logistics Series A ($35M), 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

The Opportunity

Relay's network runs on forecasts. How many parcels arrive tomorrow? Which ones to sort tonight? How many shifts to release? When those forecasts are wrong, the cost shows up in CPP: too few shifts and you pay surge premiums, too many and you waste capacity. Behind each forecast is a model, and those models need someone watching them, catching drift, and surfacing problems before they hit operations. As Lead Data Analyst for the Network squad, you will own the analytical oversight across Relay's forecasting systems. That means tracking model performance across demand forecasting, shift planning, dimensions, and BPP inclusion. When a model starts missing, you catch it. When forecast error costs money, you quantify it. When stakeholders say 'the forecast was wrong,' you investigate and build the case for what to fix. This is not a model-building role. The Network squad's Data Scientists build and ship the forecasting models. Your job is to ensure those models keep working, to set analytical standards for the squad, and to be the first line of defence when something breaks. You will work closely with Sortation, Middle Mile, Last Mile, Routing, and Finance to understand where forecasts are working and where they're not. Relay operates a centralised data team of around 30 data engineers, analysts, and data scientists, with analysts embedded into squads across the business. You will sit in the Network squad, reporting to the Squad Lead. Depending on your experience, this role also offers line management opportunities.

What You'll Do

  • Track forecast accuracy across all Network models: demand forecasts, shift planning forecasts, dimensions model, BPP inclusion models
  • Surface model drift and performance issues before they impact operations — you're the early warning system
  • Diagnose forecast errors by client, geography, day of week, and horizon, and quantify the CPP impact
  • Build dashboards that make model performance transparent to the squad and stakeholders
  • Set analytical direction for the Network squad: define what metrics matter, where to invest analytical effort
  • Work with Data Scientists to build the business case for model improvements, whether that's new input signals, data quality fixes, or client-specific treatment
  • Monitor how capacity matches demand across Sortation, Middle Mile, Last Mile, and track shift release effectiveness
  • Translate model performance into business impact that stakeholders can act on

Who Will Thrive in This Role?

  • You catch problems before they become crises – you're vigilant about when models start behaving unexpectedly
  • You're obsessive about data quality and forecast accuracy – you hold models accountable for their predictions
  • You're fluent in SQL and experienced with dbt/BigQuery – you can build and maintain analytical models
  • You've built dashboards that are operationally critical, not just 'nice to have'
  • You have a passion for forecasting and predictive modelling – you're energised by understanding where predictions hit and where they miss
  • You translate technical performance into business impact – CPP, surge costs, wasted capacity
  • You're comfortable challenging Data Scientists when models aren't performing, and working with them to improve
  • You communicate clearly with operations teams and can explain what the data shows without the jargon
  • You have at least 5 years of experience as a data analyst or similar role
  • You care about impact more than insight – you measure success by whether forecasts drive better decisions

Who Thrives at Relay?

  • 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.

If these resonate, and you combine strong technical fundamentals with entrepreneurial drive, let’s connect. Relay is an equal-opportunity employer committed to diversity, inclusion, and fostering a workplace where everyone thrives.

Lead Data Analyst - Network employer: Relay

Relay is an exceptional employer, offering a vibrant and intellectually stimulating work culture that prioritises innovation and collaboration. With a strong focus on employee growth, team members are encouraged to take ownership of their roles and contribute to meaningful projects that reshape the logistics industry. Located in a fast-paced environment backed by significant investment, Relay provides unique opportunities for professional development and the chance to make a tangible impact on the future of e-commerce.

Relay

Contact Details:

Relay Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Lead Data Analyst - Network

Tip Number 1

Network, network, network! Get out there and connect with people in the industry. Attend meetups, webinars, or even just grab a coffee with someone who works at Relay or in logistics. You never know who might have the inside scoop on job openings!

Tip Number 2

Show off your skills! Create a portfolio of your analytical work, including dashboards and models you've built. When you get the chance to chat with potential employers, share your insights and how you've tackled forecasting challenges in the past.

Tip Number 3

Prepare for interviews by diving deep into Relay's mission and values. Think about how your experience aligns with their goals of reducing friction in commerce. Be ready to discuss how you can contribute to their vision of seamless online shopping.

Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in being part of the Relay team.

We think you need these skills to ace Lead Data Analyst - Network

Forecast Accuracy Tracking
Model Performance Monitoring
Data Quality Assurance
SQL
dbt
BigQuery
Dashboard Development

Some tips for your application 🫡

Tailor Your CV:Make sure your CV speaks directly to the Lead Data Analyst role. Highlight your experience with forecasting, data quality, and any relevant tools like SQL or BigQuery. We want to see how your skills align with our mission at Relay!

Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for data analysis and how you can contribute to our team. Be sure to mention specific examples of how you've tackled forecasting challenges in the past.

Showcase Your Analytical Skills:In your application, don’t just list your skills—demonstrate them! Include examples of dashboards you've built or models you've monitored. We love seeing how you've made an impact in previous roles.

Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy!

How to prepare for a job interview at Relay

Know Your Forecasting Models

Before the interview, brush up on the forecasting models relevant to the role. Understand how they work and their impact on operations. Be ready to discuss specific examples of how you've tracked model performance or diagnosed forecast errors in your previous roles.

Showcase Your SQL Skills

Since fluency in SQL is crucial for this position, prepare to demonstrate your skills. You might be asked to solve a problem using SQL during the interview, so practice writing queries that could help analyse data quality or forecast accuracy.

Prepare to Discuss Data Quality

Relay values data quality highly, so come prepared with examples of how you've ensured data integrity in past projects. Be ready to explain how you would approach diagnosing issues with model drift and what steps you would take to improve data quality.

Communicate Clearly and Confidently

You’ll need to explain complex data insights to non-technical stakeholders. Practice articulating your thoughts clearly without jargon. Think about how you can translate technical performance into business impact, as this will be key in your discussions.