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Ux Researcher à Espagne

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Staff Data Scientist

Faites partie des premiers candidats.
Relay
Londres
GBP 80 000 - 110 000
Faites partie des premiers candidats.
Il y a 2 jours
Description du poste
Staff Data Scientist @ RELAY

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 (whose portfolio spans fusion energy and space exploration), 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

THE TEAM
• ~90 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

Work Alongside Industry Leaders

Andy Turner - Director of Data

Andy Turner has built and led data teams across global enterprises and high growth scale ups on five continents. He has delivered cloud native platforms, launched AI products end to end, and holds patents for novel machine learning applications in the UK Capital Markets. Trained in statistics at Oxford, he pairs strong technical fundamentals with clear judgement, a commercial focus, and a bias to deliver.

Gavin Sutton - Staff Data Scientist

Gavin Sutton is Staff Data Scientist at Relay, with a strong track record of building geospatial and predictive systems that drive strategic business decisions. Gavin developed Rightmove's Automated Valuation Model, used daily by major UK lenders, and has since led the design of GIS platforms, patent-pending ML algorithms, and cloud-native ML pipelines across sectors. With deep experience in modelling, mapping and modern ML tooling, he brings precision, creativity and a delivery mindset to complex data problems.

Relay's Mission

Relay exists 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.

In this future:
  • Delivery feels invisible-free shipping is the default, and logistical friction disappears from consumers' minds.
  • Retailers of all sizes thrive equally online, whether selling a £5 item or a £500 one, as delivery becomes universally efficient and cost-effective.
Just as the internet eliminates friction from communication, Relay removes friction from the movement of goods, enabling broader participation and creating new opportunities for merchants and consumers alike.

Tech Stack Highlights

  • Cloud-native on GCP with extensive use of BigQuery and Cloud Run
  • Extensive use of ML modelling and LLM inference - no gimmicks here, this is our daily routine
  • Python, Rust and TypeScript - we keep things simple but use the right tool for the job
  • Cross-platform Flutter apps with a deep focus on user experience
  • Emerging tech integrations, including robotics and IoT-powered operations
The Opportunity

The Delivery Quality squad is Relay's first line of defence against delivery failure. Our mission is to prevent, detect, and resolve quality issues at every stage of the network; from parcels lost in transit to last mile couriers misrepresenting deliveries. We combine rich geospatial, behavioural, and systems level signals to uncover the root causes of loss, fraud, and non compliance; then build models and infrastructure to address them before they scale.

This is one of the most technically ambitious domains at Relay; it spans predictive fraud detection, courier behaviour classification, real time alerting systems, and machine learning for proof of delivery (POD) image assessment. It is also highly operational; every marginal improvement feeds directly into cost, trust, and customer satisfaction. Example projects include:
  • Building a real time loss detection engine using bag, parcel, and location signals to flag handover breakdowns and trigger intervention
  • Developing image classifiers to assess POD validity (such as blurry, empty, or mislocated images); enabling immediate feedback and backtesting for compliance
  • Designing fraud models to identify bad actors using delivery signals, handover anomalies, and claims data; driving proactive suspensions over reactive escalations
  • Partnering with engineers to scale the signal architecture that powers the quality stack; including transition deadline monitors, courier feedback systems, and fraud detection pipelines
  • Leading the squad's data practice; setting scientific direction, mentoring other scientists and analysts, and building compoundable systems that uphold Relay's quality promise
As a Staff Data Scientist, you will operate as both an individual contributor and a technical leader. You will shape the models, data foundations, and scientific strategy that protect the integrity of the delivery network during its fastest period of growth.

Who Will Thrive in this role?

You are a Staff level data scientist who thrives in complexity. You have built models that do not just predict but intervene; shaping live systems, operational behaviour, and business outcomes. You move confidently between ambiguity and structure; from unlabelled data to production models. You set a technical direction, coach others, and care about durability as much as delivery.
  • Proven record of deploying production grade models in high stakes, real world systems
  • Deep hands on experience with computer vision; including image classification and quality scoring
  • Fluency across modelling approaches; from supervised learning and anomaly detection to fraud and behavioural models
  • Strong statistical foundations applied to noisy, fragmented, or incomplete datasets
  • Expertise in Python and SQL
  • Able to navigate weak signals, shifting constraints, and ambiguous problem definitions
  • Skilled communicator; able to align technical, product, and operational stakeholders
  • Experience mentoring or managing other data scientists or analysts to raise the bar and scale impact
Who Thrives at Relay?

Relayers share core traits, captured in our guiding principles, "The Relay Edge":
  • 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.
Compensation & Benefits

  • Generous equity, richer than 99% of European startups, with annual top-ups to share Relay's success.
  • Private health & dental coverage, so comprehensive you'd need to be a partner at a Magic Circle law firm to match it.
  • 25 days of holidays
  • Enhanced parental leave.
  • Hardware of your choice.
  • Extensive perks (gym subsidies, cycle-to-work, Friday office lunch, covered Uber home and dinner for late nights, and more).
Fast and Focused Hiring Process

  1. Talent Acquisition Interview - 30 min
  2. Technical Deep Dive - Python, ML Tooling, Modelling - 1 hour
  3. Case Study Interview - 1.5 hours
  4. Relay Operating Principles & Impact- -1 hour
  5. Decision and offer within 48 hours. Our process mirrors our pace of work.

Relay is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
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* Le salaire de référence se base sur les salaires cibles des leaders du marché dans leurs secteurs correspondants. Il vise à servir de guide pour aider les membres Premium à évaluer les postes vacants et contribuer aux négociations salariales. Le salaire de référence n’est pas fourni directement par l’entreprise et peut pourrait être beaucoup plus élevé ou plus bas.

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