Staff Applied Scientist, Causal Inference

Relay; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Relay is building the future of logistics in a data-driven online era. As Staff Applied Scientist, you will set the standard for causal inference, measure the impact of decisions on couriers over time, and shape experiments across a live two-sided marketplace.

You will adapt advanced causal inference, statistics and ML techniques to real-world problems, productionize models, and collaborate with Operations Research to optimise network-wide performance.

Qualifications

• Bring deep expertise in causal inference, experimentation and ML, from PhD, publications, or frontier industry work.
• Experience applying advanced techniques to real marketplaces or products and owning outcomes.
• Ability to think in mechanisms and let evidence guide decisions.

Responsibilities

• Set the standard for causal inference at Relay.
• Measure how decisions affect each courier, today and over time.
• Shape how the company experiments across a live, two-sided marketplace.
• Adapt advanced techniques across causal inference, statistics and ML to company problems and see them through to production.
• Partner with Operational Research to embed insights into network-wide optimisation and contribute to the research culture.

Skills

Causal inference

Experimentation

Machine learning

Statistics

Communication

Education

PhD or publications

Tools

Operational Research

Production ML systems

Job description

the company 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), the company is scaling faster than 99% of venture-backed startups. We're assembling the most talent-dense team the logistics industry has ever seen

the company’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

the company 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 the company 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 the company 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 the company's network-wide optimisation.

The OpportunityAs a Staff Applied Scientist at the company, you'll:

• Set the standard for causal inference at the company.

• Measure how our decisions affect each courier, today and over time.

• Shape how the company experiments across a live, two-sided marketplace.

• Adapt advanced techniques across causal inference, statistics and machine learning to the company's problems, and see them through to production.

• Partner with Operational Research to embed your insights and models into the company'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 the company?

• 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.

the company is an equal-opportunity employer committed to diversity, inclusion, and fostering a workplace where everyone thrives.