Head of Finance AI Transformation

Relay Technologies
London - On-sitePosted 16 April 2026

Tech Stack

Job Description

Head of Finance AI Transformation 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 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 • ~110 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 The Opportunity We're building a finance function that scales faster than the business — not by adding headcount, but by designing a system where AI, automation, and clean architecture do the heavy lifting. We're looking for someone to build that system from scratch: the finance stack, the AI workflows, and the operating model that ties it all together. Reporting to our CFO, you'll work directly with founders and functional leaders to turn Relay's finance function into a real-time decision engine. This is not a "transformation" role in the traditional sense. This is a 0 to 1 build role with direct leverage on company velocity. What you’ll own - Design the finance function from first principles. Own how finance operates in an AI-native, high-growth company — not as an improvement on legacy systems, but as a definition of the default state. Rebuild core processes (R2R, P2P, O2C, FP&A) to eliminate manual work before it takes root. - Deploy AI across every high-leverage workflow Identify and ship use cases in weeks, not quarters — AI-driven forecasting and scenario modelling, automated reporting and commentary, close and reconciliation workflows, and collections, risk, and anomaly detection. - Build and own the technical stack Select the minimum viable, maximum scalable architecture. Make strong calls on ERP vs lightweight tooling, build vs buy, and data design. Ensure everything is modular, API-friendly, and AI-ready from day one. - Work directly with engineering and data teams Translate financial logic into production systems. This is a hands-on, cross-functional role — you're putting ideas into production, not handing them over. - Redesign the operating model around augmentation Define how humans and AI work together — including which roles don't need to exist. Build a function where one great operator and AI outperforms five traditional hires. - Deliver the outcomes leadership needs Reduce time to close, time to forecast, and time to decision. Give leadership real-time, high-quality financial insight, and build systems that scale from current revenue to £100M+ without breaking. Who will thrive in this role - You've built or redesigned finance systems and operating models end-to-end — not just advised on them. - You've deployed automation or AI in real finance workflows. Production, not strategy decks. - You're fluent in the TA and Ops/data hybrid: FP&A, R2R, funnel diagnostics, and partnering with RevOps/FP&A to forecast and drive decisions. - You think in systems, not processes — and you're comfortable making decisions with incomplete information. - You've worked closely with CFOs, founders, or operators on scaling finance in high-growth environments. - Proven experience owning finance programs and delivering systems implementations end-to-end - Hands-on deployment of AI or automation in real finance workflow ... (truncated, view full listing at source)
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