Top Tech Jobs & Startup Jobs in London

Design and build low-latency, real-time liquidity observability systems and streaming data pipelines for global financial operations. Architect Kafka and Flink pipelines, develop real-time aggregation and event-driven architectures, model streaming and analytical data, and ensure reliability through idempotency, exactly-once processing, failure recovery, and schema evolution. Collaborate with product, data science, quantitative analysis, and treasury teams to support liquidity positioning, forecasting, and operational visibility.

Leads the development and deployment of machine learning models for Wise’s Receive product, using customer and transaction data to improve risk management, prioritization, personalization, and customer outcomes. Designs experiments, builds production-grade data pipelines and modelling tools, analyzes trends and anomalies, and collaborates with engineering, product, analytics, operations, and risk teams. The role also supports scalable data science capabilities and fraud prevention across global markets.

Own global executive searches for engineering, product, data, and design leadership roles. Advise senior stakeholders on hiring, succession, organization design, leveling, and location strategy. Build leadership pipelines, assess and close executive candidates, improve recruitment processes, analyze market intelligence, and develop AI-driven talent solutions. Mentor recruiting colleagues and promote inclusive hiring practices.

Design and scale distributed, event-driven systems for AML, customer due diligence, and financial crime prevention across European and UK markets. Own risk architecture end to end, make critical technical decisions, mentor engineers, partner with Product, Compliance, and Data Science, and deliver real-time, auditable risk decisions using high-throughput systems.

Own commercial growth for Wise Platform across Nordic financial institutions. Develop market strategy, identify and engage banks and fintechs, manage enterprise sales cycles, negotiate high-value partnerships, and close contracts. Translate Nordic market needs into product and regional expansion insights while supporting technical product integrations with clients.

Manage safeguarding implementations for new products and legal entities across Wise’s global finance and treasury operations. Lead reviews, approvals, integrations, controls setup, documentation, testing, and delivery. Coordinate product, engineering, analytics, compliance, finance, treasury operations, and regional teams. Investigate incidents, drive resolution, maintain safeguarding calculations and product-flow documentation, and ensure controls support regulatory and customer-fund protection requirements.

Own the Wise Platform API access, security, webhook infrastructure, and tooling roadmap. Partner with engineering, security, compliance, support, and enterprise customers to improve authentication, reliability, observability, diagnostics, and self-service troubleshooting. Define metrics and deliver platform improvements that reduce incidents, support demand, authentication failures, and resolution times while strengthening security and enabling scalable enterprise integrations.

Lead end-to-end global payroll expansion projects, managing plans, timelines, risks, vendors, readiness assessments, testing, go-live sign-off, stakeholder communications, and post-implementation support across new and existing markets.

Leads product analytics for Wise Platform’s Online Platforms vertical, owning growth and performance metrics, opportunity sizing, product discovery, and strategic recommendations. Builds the analytics function, data foundations, reporting, and operating model while managing and developing product analysts. Partners with product, engineering, operations, and commercial leaders to influence roadmaps, prioritize opportunities, and drive measurable business impact.

Lead development and deployment of machine learning models that detect account takeover and send scams. Analyze transaction and event-log data, build real-time detection pipelines, design experiments, identify fraud patterns, and collaborate with engineering, security, and product teams. The role also involves mentoring junior data scientists, communicating insights to nontechnical stakeholders, and applying methods including anomaly detection, supervised and unsupervised learning, deep learning, graph-based solutions, and statistical analysis.