Marketplace Optimisation Lead

As our next Marketplace Optimisation Lead, you'll play a key role in helping us build the UK's go-to home improvement marketplace. Analytics tooling — including AI — is doing more of the heavy lifting on execution, and this role is the human layer above it: making sure the numbers are right.

This role is perfect for someone with data science experience who enjoys working with data scientists on building models and systems with huge impact but also wants to own the strategy and direction of their work and work closely with the C-suite on driving the company forward with more intelligent, data-driven systems

Forget long lists, here's what you'll actually spend your time on:

• Owning metric integrity — defining and governing what our numbers actually mean, so no one downstream is making a call off a flawed definition or a dodgy query.

• Acting as the quality gate on AI-generated analysis — checking the logic, validating the output, and catching errors before they ever reach a stakeholder.

• Designing and interpreting A/B tests — setting the success metrics, sizing the sample properly, and knowing when a result is underpowered rather than just running with it.

• Diagnosing what's actually driving commercial performance — why revenue is moving, where leads are dropping out of the funnel, which segments are underperforming — and turning that into a structured investigation with real hypotheses.

• Turning analysis into action — sizing the commercial impact, laying out the trade-offs, and helping the business decide what to do differently, then tracking whether it worked.

• Building interactive dashboards and self-serve tools that keep stakeholders oriented day-to-day, without an analyst needing to be in the loop every time.

It's not about ticking every box. It's about turning up curious, ready to crack on. You'll do well here if you bring:

• A track record across all three layers — not just producing analysis, but diagnosing the cause and driving the action that actually changed the outcome.

• The analytical foundation to sense-check AI-generated work — fluency with SQL, Python and data concepts, so you know when an output is wrong before anyone else spots it.

• Experience designing experiments and turning statistical findings into commercial recommendations that hold up under scrutiny.

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