We’re looking for a Lead Data Scientist to join our growing Receive Team in London.  This role is a unique opportunity to work behind the scenes of company transactions, understand how we mitigate risk and at the same time provide our customers with the seamless service they deserve. What you build will have a direct impact on Wise’s mission and millions of our customers. Role Overview: As a Lead Data Scientist on the Receive team, you will leverage your expertise in data science to identify, design and deploy models that improve how customers receive and add money with Wise. Your work will help us make better decisions, improve customer outcomes, and build scalable data science capabilities for a growing product area. You will collaborate closely with cross-functional teams, including engineering, product, analytics, operations and risk management.  Key Responsibilities: Lead the development and deployment of machine learning models and data science solutions to improve Receive product performance across different Wise markets Analyse large volumes of customer, transaction and product data to identify trends, patterns, risks and opportunities Design and implement experiments to evaluate the effectiveness of product changes, decisioning systems and customer experience improvements Build scalable modelling approaches that support better prioritisation, personalisation, risk management and operational decision-making Collaborate with analysts, product managers, engineers, operations and risk teams to translate business requirements into actionable data science solutions Develop robust data pipelines, algorithms and tools to support production-grade modelling and decision-making Stay informed about the latest advancements in data science, machine learning, and payment fraud prevention techniques to ensure state-of-the-art capabilities in the Spend domain A bit about you:  Proven track record of deploying models from scratch, including data preprocessing, feature engineering, model selection, evaluation, and monitoring Solid knowledge of Python, and ability to make and justify design decisions in your code. You know how to use Git to collaborate with others (e.g. opening Pull Requests on GitHub) and are able to review code. Ability to read through code, especially Java. Demonstrable experience collaborating with engineering on services Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL) Familiarity with anomaly detection, supervised and unsupervised learning methods, and real-time data analysis 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 Some extra skills that are great (but not essential):   Experience with MLOps tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CD Prior experience in the fraud domain and a strong understanding of fraud detection techniques Experience designing and deploying LLM-based solutions in production 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.