Lead AI Data Scientist - Servicing at Wise

About the Role:  The Servicing team offers an exciting environment for applying cutting-edge Generative AI solutions. This team is dedicated to enhancing our financial crime mitigation operations through tooling and automation, aiming to streamline reviews and simplify the work of our operations staff. A core focus involves analyzing and automating significant parts of our operational workflows. Furthermore, the team develops LLM-based tools to assist crime prevention teams in deflecting demand. The successful candidate will have the chance to directly contribute to Wise's mission by tackling these challenges and developing a comprehensive testing suite for our solutions.   Here’s how you’ll be contributing:  • End-to-End Automation: Lead the development and deployment of AI models designed to augment operational workflows, specifically targeting the automation of case comments, red flag generation, final review summaries, and data labeling. • Full-Stack Deployment: Take ownership of the production pipeline by writing and deploying production-ready Python services. You must be willing to bypass engineering bottlenecks to ship value quickly while maintaining code quality. • Human-in-the-Loop Architecture: Design systems where AI provides recommendations and drafts, ensuring human operators retain the final decision-making authority for critical financial crime mitigation assessments. • Rigorous Testing & Governance: Establish comprehensive testing frameworks (e.g., shadow mode, A/B testing) for production environments and act as the technical liaison with Compliance to ensure all models meet regulatory standards prior to launch. • Strategic Demand Deflection: Go beyond ticket handling by analyzing upstream data to create strategies that deflect financial crime attempts before they reach the operations team, effectively reducing manual workload. • Mentorship & Leadership: Lead and grow other Junior Data Scientists, fostering a product-focused mindset and guiding them through complex technical implementations and architectural decisions.   A bit about you:  • Experience implementing, training, testing and evaluating performance of Machine Learning systems; • Strong Python knowledge. A big plus for proven familiarity and experience with OOP principles; • Knowledge and experience developing Unsupervised Learning methods; • Experience with statistical analysis, and ability to produce well-designed experiments; • 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.   Some extra skills that are great (but not essential):     • Hands-on experience training Neural Network models and deploying them into production • Familiarity with automating operational processes via technical solutions, for example Large Language Models • Experience implementing fine-tuning, reinforced learning alignment and evaluation techniques within an LLM training pipeline. • Familiarity with agentic frameworks such as LangGraph or similar. • Willingness to get hands dirty reading many, many historical operational cases.   We’re people without borders — without judgement or prejudice, too. We want to work with the best people, no matter their background. So if you’re passionate about learning new things and keen to join our mission, you’ll fit right in. Also, qualifications aren’t that important to us. If you’ve got great experience, and you’re great at articulating your thinking, we’d like to hear from you.   And because we believe that diverse teams build better products, we’d especially love to hear from you if you’re from an under-represented demographic.