Senior Data Scientist

About the role

You will own the design and execution of advanced pricing models that directly govern how parcels move through the Relay marketplace. This role demands that you investigate complex pricing dynamics and translate them into robust production systems that define route intelligence. You will build and maintain the core models that power daily pricing decisions across the network. You will experiment rigorously to measure the impact of every model improvement and strategic pricing change. You will work as an equal partner alongside Operational Research Scientists, Analysts, and Engineers within a focused squad. Your work will ensure that thousands of daily pricing decisions are informed by the strongest possible intelligence. You will be expected to communicate your findings clearly to non-technical stakeholders without diluting the rigor. If you thrive on solving high-stakes problems with direct operational impact, this is your role.

Key facts

What you'll do

Investigate and master the intricate pricing dynamics of the Relay marketplace to define how routes are priced.

Build and maintain production-grade models that directly inform thousands of daily pricing decisions for the network.

Experiment systematically and measure the impact of model improvements and pricing strategy changes with precision.

Work as an equal team member alongside Operational Research Scientists, Analysts, and Engineers to define decision logic.

Translate complex analytical findings into clear narratives for stakeholders with varying technical backgrounds.

Own the technology stack required to automate pricing intelligence at scale within the marketplace.

Collaborate closely with operations to ensure models reflect real-world constraints and opportunities.

Use first-principles thinking to dissect pricing problems and engineer innovative data solutions.

Leverage thousands of data points captured for every parcel to refine models continuously.

Drive the definition of pricing strategies through data-led hypothesis creation and validation.

Ensure that all modeling work aligns with the broader mission of reducing friction in e-commerce logistics.

Contribute to a culture of relentless experimentation and tight feedback loops within the data team.

Requirements

You are a strong modeller who thinks in systems and understands how variables interact within a dynamic marketplace.

You care about solving the problem above all else and are motivated by impact rather than just analytics.

You love working with smart, motivated people and thrive in a fast-paced, intellectually vibrant environment.

You are fluent in python and SQL, with the ability to write clean, production-ready code.

You communicate inclusively with non-technical team members to ensure alignment and shared understanding.

You are successful working with high autonomy and minimal oversight in a rapidly scaling startup.

You love seeing the impact of your work play out in daily operations and logistics decisions.

You have experience modelling marketplace dynamics, pricing strategies, or another relevant domain in logistics or commerce.

You hold a Master's or PhD in a quantitative field such as computer science, mathematics, or operations research.

You bring a proven track record of building statistical and machine learning models to solve complex business problems.

You are comfortable handling large datasets and writing efficient queries to extract insights.

You understand software engineering best practices and care about maintainable, scalable solutions.

You are comfortable deploying models into production and monitoring their performance over time.

You have strong written and verbal communication skills to articulate technical concepts to diverse audiences.

Nice to have

Experience with marketplace economics or last-mile delivery optimization is preferred.

Familiarity with logistics or e-commerce datasets would accelerate your contribution to the team.

Knowledge of modern MLOps tools and cloud platforms is an advantage for production deployment.

Experience with A/B testing frameworks and causal inference methods is valued for measuring impact.

Practical notes

The role is based in Shoreditch, London, operating on a hybrid schedule with 4 days on-site and 1 day remote.

Full-time engagement is required for this position.

The office provides extensive perks including gym subsidies, cycle-to-work schemes, and regular team socials.