Lead Data Scientist – Fraud Risk

Overview

As Lead Data Scientist on Wise’s Receive team in London, you will build and deploy machine learning models to improve how customers receive and add money, guiding risk-aware and customer-centric decisions. You’ll work with cross-functional partners to scale data science capabilities and directly impact millions of users. Expect to design experiments, develop robust data pipelines, and stay at the forefront of fraud prevention and payment analytics. This role offers meaningful impact in a fast-growing product area and a mission-driven culture.

Pay / Benefits

• Stock options
• Hybrid working model
• Annual personal development budget
• Visa and relocation support

Responsibilities

• Lead the development and deployment of ML models to improve Receive product performance across markets
• Analyze large datasets to identify trends, risks and opportunities
• Design experiments to evaluate product changes and customer experience
• Build scalable modelling approaches for prioritisation, personalisation, risk management, and operational decision-making
• Collaborate with analysts, product managers, engineers, operations and risk teams to translate requirements
• Develop robust data pipelines, algorithms and tools for production-grade modelling and decision-making
• Stay up-to-date with advanced data science, ML and fraud prevention techniques in the Spend domain

Key requirements

• Proven track record deploying models from scratch (preprocessing, feature engineering, evaluation, monitoring)
• Strong Python skills and ability to justify design decisions; experience with Git/GitHub and reading Java code
• Experience with large datasets and data processing tech (Hadoop, Spark, SQL)
• Familiarity with anomaly detection, supervised/unsupervised learning, real-time analysis
• Strong statistical analysis and ability to present insights to non-technical audiences
• Product mindset with ability to work independently in cross-functional environments
• Excellent communication and problem-solving skills

• strong communication
• collaboration across teams
• problem solving
• Python
• Git / GitHub
• Java reading ability

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