At a Glance
- Tasks: Lead optimisation for last-mile delivery, making smart routing, pricing, and scheduling decisions.
- Company: Relay, a fast-growing logistics startup reshaping e-commerce delivery.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on collaboration and continuous improvement.
- Why this job: Join a talented team and tackle complex problems in real-time logistics.
- Qualifications: Expertise in operational research or advanced quantitative disciplines.
The predicted salary is between 56700 - 69300 £ per year.
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, and a recently closed Series B ($95M), Relay is scaling faster than 99% 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
- ~160 people, around 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
- Millions of parcels, thousands of couriers, and routing, pricing and scheduling decisions needed in real time and under uncertainty.
Relay's last mile is a growing optimisation problem that never sits still. Relay's in-house solver has carried the business through more than 100x growth, built by a team of Operational Research Scientists who designed the decision architecture the network runs on. You'll join them and take it further.
Optimisation Highlights
- An in-house solver, built and owned by our own Operational Research team, running live at scale.
- Routing, pricing and scheduling decisions made in real time and under uncertainty.
- ML and recommender intelligence integrated into the decision models.
- A flexible network: pitstops can change, warehouse automation can be reconfigured, and new processes roll out overnight.
The Opportunity
As a Quantitative Researcher at Relay, you'll:
- Lead optimisation for Relay's last mile, making the best routing, pricing and scheduling decisions for couriers and the wider network.
- Model and optimise the decisions that define our last-mile delivery network and marketplace.
- Develop our solvers and algorithms so they perform live, and at scale.
- Partner with other Scientists to unite ML and recommender systems with Operational Research.
- Raise the standard for Operational Research at Relay, and grow the team, the research culture and our voice in the tech community.
We're looking for candidates who…
- Bring deep expertise in operational research, whether from a PhD, publications, or years at the frontier in industry.
- Come from operational research or industrial engineering, or from an advanced quantitative discipline, including trading, that has branched into optimisation.
- Have built optimisation systems or ML models that run in production, and owned the outcome.
- Are energised by large, messy problems, and know how to bring structure to them.
- Collaborate generously, and explain complex ideas simply.
Don't tick every box? If this problem excites you, we'd still love to hear from you.
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.
Quantitative Researcher, Optimisation in London employer: Relay
Relay is an exceptional employer, offering a dynamic work environment at our London Sort Centre where innovation meets operational excellence. With a strong focus on employee growth and development, we foster a culture of collaboration and continuous improvement, ensuring that every team member has the opportunity to contribute to our mission of transforming logistics. Our commitment to diversity and inclusion, combined with competitive benefits and a fast-paced, intellectually stimulating atmosphere, makes Relay a truly rewarding place to work.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher, Optimisation in London
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We think you need these skills to ace Quantitative Researcher, Optimisation in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
