Lead Data Scientist – Fraud Prevention

Lead Data Scientist – Fraud Risk Team

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, the team is responsible for developing and enhancing fraud detection systems. Software engineers, data analysts, and data scientists collaborate daily to continuously improve our systems and support the fraud investigation team.

Our Vision

• 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.
• Help maintain our existing machine learning algorithms, while improving them and developing new intelligence to stop fraudsters.

Key Responsibilities

Model Maintenance and Improvement

• Maintain and optimise existing risk models to ensure their accuracy and reliability.
• Continuously monitor model performance and implement improvements based on feedback and testing.

Innovate And Develop

• Lead the development and deployment of machine learning models, features and help deploy intelligence to production.

Data Analysis & Intelligence Creation

• Conduct thorough data analysis to identify trends, patterns, and anomalies that can aid in risk mitigation.
• Develop actionable intelligence and insights to inform the Fraud Risk Team’s strategies.

Collaboration & Communication

• Work closely with the Fraud Risk Team to understand business processes and risk factors.
• Communicate complex data findings and insights effectively to non‑technical stakeholders.

Risk Reduction Initiatives

• Identify opportunities to reduce the impact of risks on good customers through data‑driven strategies and interventions.
• Develop and test strategies to balance risk mitigation with customer satisfaction.

Documentation & Reporting

• Document the development and maintenance processes for models and features.
• Prepare and present detailed reports and dashboards that reflect risk assessment outcomes and model performance.

Qualifications

• Proven track record of deploying models from scratch, including data preprocessing, feature engineering, model selection, evaluation, and monitoring.
• Strong Python knowledge. Ability to read through code, especially Java. Demonstrable experience collaborating with engineering on services.
• Experience with statistical analysis and good presentation skills to drive insight into action.
• A strong product mindset with the ability to work independently in a cross‑functional and cross‑team environment.
• Good communication skills and ability to get the point across to non‑technical individuals.
• Strong problem‑solving skills with the ability to help refine problem statements and figure out how to solve them.

Skills

• Experience working with non‑supervised algorithms.
• Prior experience in the fraud domain and a strong understanding of fraud detection techniques.

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