Lead Data Scientist - Fraud Risk

We’re looking for a Lead Data Scientist to join Wise’s growing Receive Team in London. This role is a unique opportunity to work behind the scenes of company transactions, understand how we mitigate risk and provide customers with a seamless service. What you build will have a direct impact on Wise’s mission and millions of customers.

As a Lead Data Scientist on the Receive team, you will leverage your expertise in data science to identify, design and deploy models that improve how customers receive and add money with Wise. Your work will help us make better decisions, improve customer outcomes and build scalable data science capabilities for a growing product area. You will collaborate closely with cross-functional teams, including engineering, product, analytics, operations and risk management.

Key responsibilities include: - Leading the development and deployment of machine learning models and data science solutions to improve Receive product performance across different Wise markets. - Analysing large volumes of customer, transaction and product data to identify trends, patterns, risks and opportunities. - Designing and implementing experiments to evaluate the effectiveness of product changes, decisioning systems and customer experience improvements. - Building scalable modelling approaches that support better prioritisation, personalisation, risk management and operational decision-making. - Collaborating with analysts, product managers, engineers, operations and risk teams to translate business requirements into actionable data science solutions. - Developing robust data pipelines, algorithms and tools to support production-grade modelling and decision-making. - Staying informed about advances in data science, machine learning and payment fraud prevention techniques to ensure state-of-the-art capabilities in the Spend domain.

The successful candidate will have a proven track record of deploying models from scratch, including data preprocessing, feature engineering, model selection, evaluation and monitoring. They will have solid knowledge of Python, be able to make and justify design decisions in their code, use Git to collaborate with others and review code, and be able to read through code, especially Java. Demonstrable experience collaborating with engineering on services is also required.

You should have experience working with large datasets and data processing technologies such as Hadoop, Spark and SQL; familiarity with anomaly detection, supervised and unsupervised learning methods and real-time data analysis; experience with statistical analysis and presenting insights; a strong product mindset; good communication and problem-solving skills; and the ability to work independently in a cross-functional and cross-team environment.

Desirable additional experience includes MLOps tools such as Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR and CI/CD; experience in the fraud domain and a strong understanding of fraud detection techniques; and experience designing and deploying LLM-based solutions in production.

Wise welcomes people from all backgrounds and values diverse, equitable and inclusive teams. Qualifications are not the only consideration; relevant experience and the ability to articulate your thinking are valued.

Key benefits include stock options in a profitable company, a hybrid working model with flexibility to work from home or overseas, an annual personal development budget for books, courses or conferences, and visa and relocation support.

Compensation: GBP 90,500–GBP 127,000 yearly.