Top Tech Jobs & Startup Jobs in London

Lead ML strategy and delivery for Workforce Management: forecasting, routing, assignment, escalation and time-to-completion prediction. Build, ship and maintain production ML systems, coach and grow a team of ML scientists, work cross-functionally to turn operational problems into pragmatic ML solutions, and improve customer and operational outcomes.

The Staff Product Designer will lead design projects, set long-term design vision, mentor designers, and collaborate across teams to enhance customer experiences at Monzo.

Lead commercial strategy for Monzo Business Banking: define pricing, business models, and go-to-market plans; build business cases, monitor financial performance, and drive product and distribution decisions to accelerate growth.

Lead the technical accounting policy function, provide expert advice on complex accounting issues, and manage new accounting standards adoption and stakeholder communications.

Run end-to-end hiring processes for tech roles: partner with hiring managers to define skills and assessments, source diverse candidates, ensure excellent candidate experience, improve processes using data, and close hires. 12-month FTC, London or remote (UK) with occasional office travel.

Lead a distributed engineering organisation across multiple European markets, develop senior engineering leaders, partner with cross-functional stakeholders to define and execute European strategy, shape market expansion, localisation and regulatory approach, drive alignment across teams, design scalable operating models, and help validate product-market fit while balancing short-term delivery and long-term strategy.

Lead and grow a team of ML scientists to build and productionise credit underwriting, pricing, limits, and lifecycle models. Define Borrowing's ML roadmap for UK growth, new segments, and EU expansion, improve feature engineering and data use, and raise standards for model quality, governance, and iteration speed.

Lead design and governance of Borrowing's data layer: architect data products, build reusable feature layers, scale analytics engineering infrastructure, define SLAs/contracts, and partner across product, credit, engineering, and Data Platform to ensure consistent, production-grade datasets for analytics, ML, decisioning, and regulatory use.

Build and optimise downstream data pipelines and models (mostly in BigQuery) to support reporting, ML, financial and regulatory use cases. Define and apply data warehouse best practices, improve data quality, reduce complexity and cost, and support BI (Looker) and analytics consumers across the business.

Partner with Payments leadership to manage P&L and balance sheet, build business cases, analyse product and market performance, drive commercial projects, forecast and report results, design processes, and mentor financial analysts to support strategic financial decision-making.