At a Glance
- Tasks: Lead causal inference and shape experiments in a dynamic marketplace.
- Company: Join Relay, a fast-growing logistics startup reshaping e-commerce.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and continuous improvement.
- Why this job: Make a real impact on how goods move online and enhance the shopping experience.
- Qualifications: Expertise in causal inference, statistics, and machine learning required.
The predicted salary is between 63000 - 77000 £ 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
Relay makes millions of dynamic, interconnected decisions across its network every day, and each one shapes the experience of the thousands of couriers who work with us. Knowing what any one of those decisions actually did is hard: couriers interfere with each other, effects vary from person to person, and the outcomes that matter compound over time. Science at Relay is the brain of the operation: machine learning, operational research, recommender systems, causal inference, location intelligence and experimentation, working together to decide how every parcel gets delivered. You'll lead causal inference at Relay from the start.
Causal Inference Highlights
- A live, two-sided marketplace to experiment in, with every decision logged across millions of parcels.
- Interference between couriers, heterogeneous effects and long-term outcomes are the norm here, not the exception.
- Four marketplaces, each with its own app and its own economics.
- Your insights and models feed straight into Relay's network-wide optimisation.
The Opportunity
As a Quantitative Researcher at Relay, you'll:
- Set the standard for causal inference at Relay.
- Measure how our decisions affect each courier, today and over time.
- Shape how Relay experiments across a live, two-sided marketplace.
- Adapt advanced techniques across causal inference, statistics and machine learning to Relay's problems, and see them through to production.
- Partner with Operational Research to embed your insights and models into Relay's network-wide optimisation, and help build our research culture and voice in the tech community.
We're looking for candidates who…
- Bring deep expertise in causal inference, experimentation and machine learning, whether from a PhD, publications, or years at the frontier in industry.
- Come from statistics, an experimental science such as biology or psychology, or quantitative research.
- Have applied advanced techniques to real marketplaces or products, and owned the outcome.
- Think in mechanisms, and let evidence guide decisions.
- Are energised by open, interconnected problems, and know how to find the signal.
- 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, Marketplace 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, Marketplace in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Quantitative Researcher, Marketplace 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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Relay, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Relay. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Relay
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Relay!
✨Prepare for Case Studies
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.
