Lead Data Scientist - Fraud Prevention

Location: Global

Team: General

About the Role

Join Wise's Fraud team as a Lead Data Scientist, where you'll safeguard our global platform against financial crime while protecting legitimate customers. Leveraging cutting-edge machine learning, real-time monitoring, and advanced data analysis, you'll maintain and enhance fraud detection systems in close collaboration with software engineers, data analysts, and fraud investigators. Our vision is to build a scalable fraud prevention engine that exceeds regulatory expectations and utilises ML to identify risks, fostering strong partnerships across teams. In this role, you'll level up our intelligence by refining existing models, developing new features, and creating actionable insights to mitigate risks in our receiving processes. You'll conduct in-depth data analysis to uncover patterns and anomalies, communicate findings to non-technical stakeholders, and drive strategies that balance robust risk mitigation with exceptional customer experience. Help grow our data science team as we innovate to keep Wise secure for everyone, everywhere. We're seeking a motivated leader with a proven track record in model deployment, strong Python skills, and a product mindset. Experience in fraud detection is a plus, but passion for problem-solving and diverse teams is key. At Wise, qualifications matter less than your experience and ability to articulate impact—no borders, no prejudice, just building the future of money together.

Key Responsibilities

• Maintain and optimise existing risk models to ensure their accuracy and reliability
• Continuously monitor model performance and implement improvements based on feedback and testing
• Lead the development and deployment of machine learning models, features and help deploy intelligence to production
• Conduct thorough data analysis to identify trends, patterns, and anomalies that can aid in risk mitigation
• 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
• Identify opportunities to reduce the impact of risks on good customers through data-driven strategies and interventions
• Document the development and maintenance processes for models and features

Required 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

Preferred Qualifications

• Experience working with unsupervised algorithms
• Prior experience in the fraud domain and a strong understanding of fraud detection techniques

Required Skills

• Python
• Java code reading
• Machine learning model deployment
• Data preprocessing
• Feature engineering
• Statistical analysis
• Model evaluation and monitoring
• Cross-functional collaboration
• Problem solving
• Presentation and communication

Benefits

• RSUs (stock options)
• Flexible working
• Parental leave
• Learning budget
• Paid sabbatical after 4 years
• Health insurance
• Company retreat
• Wise card

Wise is an equal opportunity employer committed to building a diverse workforce.