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As a Staff Product Designer at Wise, you'll design user-friendly mobile and web experiences, focusing on FinCrime products while collaborating with product teams to enhance customer interactions and streamline operational processes.

Lead and mentor a content design team to enhance the user experience of a financial platform, collaborating on high-impact projects while maintaining alignment and quality across products.

Lead Wise's global card partnerships and strategy across Visa, Mastercard, issuers and acquirers. Own commercial negotiations, expansion into new markets, compliance-aligned card programmes, and cross-functional launches while advising Product, Engineering, Finance, Legal and Compliance.

Lead credit analytics for Wise's SME credit products: validate risk models, build forward-looking loss and delinquency forecasts, define data strategy, create dashboards and pipelines, inform market launches, and hire and coach a team of analysts.

Lead content design for Wise's Spend product, turning complex financial and risk concepts into clear, user-centred experiences. Collaborate with Product, Design, Engineering, Data, and Operations; own end-to-end content projects; elevate content quality and influence cross-squad consistency.

Build and own backend-first scam prevention systems (80% backend, 20% frontend) using Java/Spring and React. Design scalable distributed systems, CI/CD pipelines, resilient schemas, and TDD-driven code while collaborating with Product, Data Science, and Investigations to prevent financial crime.

Build and maintain reusable Android UI components and tooling for the Design System using Compose. Own feature lifecycle from discovery to release, support product teams, champion design system initiatives, ensure accessibility, performance, visual regression testing, and collaborate with cross-functional teams while mentoring new engineers.

Lead development of the label integrity layer for Risk ML: ensure label quality, define statistical metrics, build automated audits, monitor label drift, and work end-to-end on model training, evaluation, and deployment in collaboration with Risk Intelligence, Data Engineering, and Product teams.

Build and maintain the integrity layer for label data used in FinCrime ML models. Define and monitor statistical quality metrics, design automated label audit processes, and work end-to-end on model training, evaluation, and deployment in collaboration with Risk Intelligence, Data Engineering, and Product teams.

Own and measure Cloud Platform metrics end-to-end, define targets and coverage, build dashboards and frameworks, enable data-informed decisions, and influence engineering leadership to improve reliability, cost, and productivity.