The Fraud team at Wise is dedicated to safeguarding our platform against financial crime and ensuring the protection of our legitimate customers. Leveraging cutting-edge machine learning, real-time transaction monitoring, and data analysis, our team is responsible for developing and enhancing fraud detection systems. Software engineers, data analysts, and data scientists collaborate on a daily basis to continuously improve our systems and provide support to our fraud investigation team.

Our vision is:

Build a globally scalable fraud prevention and detection engine to maintain Wise as a secure environment for our legitimate customers.
Utilise machine learning techniques to identify potential risks associated with customer activity.
Foster a strong partnership between our fraud investigators and the product team to develop solutions that leverage the expertise of fraud prevention specialists.
Not only meet the requirements set by regulators and auditors but also surpass their expectations.

We are looking for a highly skilled Staff Data Scientist to lead technical innovation and drive the development of advanced data science solutions. This role is pivotal in enhancing our fraud detection capabilities and ensuring the security of our platform.

Here’s how you’ll be contributing:

Innovate and Develop: Lead the development and deployment of machine learning models, including neural networks, anomaly detection, graph-based models, Transformers.
Lead and Collaborate: Mentor team members and promote adoption of AI workflows for automation across the business. Collaborate with cross-functional teams to integrate data science solutions into Fraud prevention product offerings.
Deploy and Integrate: Develop scalable deployment strategies together with Platform teams and integrate LLMs with AI agents for seamless production use.
Optimise and Evaluate: Conduct large-scale training and hyper-parameter tuning, and define performance metrics to ensure high-quality model outputs.
Data Strategy and Management: Design and implement strategies for data collection, curation, and augmentation to support robust model training.
Documentation and Reporting: Communicate complex data findings to non-technical stakeholders effectively. Document the development and maintenance processes for models and features.

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs.

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