About the Role:
The AML team at Wise is dedicated to safeguarding our platform against financial crime, while ensuring seamless service for our legitimate customers. Leveraging cutting-edge machine learning, real-time transaction monitoring, and data analysis, our team is responsible for developing and enhancing AML detection systems which have evidenceable regional coverage of different financial crime typologies and red flags. Software engineers, data analysts, data scientists and compliance specialists collaborate on a daily basis to continuously improve our systems and provide support to our AML investigation team.
Our vision is:
Build a globally scalable AML 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 AML investigators and the product team to develop solutions that leverage the expertise of AML investigation 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 AML detection capabilities.
Here鈥檚 how you鈥檒l be contributing:
Innovate and Develop: Lead the development and deployment of machine learning models, including neural networks, anomaly detection, graph-based models, Transformers. Design and build modular detection systems able to detect in an evidenceable way red flags and typologies across different regions where Wise operates.
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 AML detection product offerings.
Deploy and Integrate: Develop scalable deployment strategies together with Platform teams and integrate LLMs with AI agents for seamless production use
Optimize and Evaluate: Conduct large-scale training and hyperparameter 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.