Data Scientist, North America Onboarding Team

Company Description

Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.

Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.

As part of our team, you will be helping us create an entirely new network for the world’s money.
For everyone, everywhere.

More about our mission and what we offer.

As a Data Scientist on the North America Onboarding team, you will leverage your expertise in data science to innovate and deploy models that ensure regulatory compliance and provide a seamless onboarding experience. Your work will directly influence our ability to mitigate risk while reducing friction for customers opening accounts globally. You will collaborate closely with cross-functional teams, including engineering, product, and risk management.

• Design, develop, and deploy machine learning models to enhance our detection of financial crime, compliance violations, and risk associated with customer onboarding (KYC) and business verification (KYB).
• Take over existing models to prevent chargebacks in North America. Ideate and work on new opportunities with ML to help reduce losses on chargebacks to reduce customer fees.
• Analyze large volumes of customer and business data to identify trends, patterns, and anomalies related to identity verification and regulatory risk typologies.
• Design and implement experiments (A/B tests) to evaluate the effectiveness of new risk controls and product features, continuously improving performance and balancing compliance with customer experience.
• Develop robust data pipelines, algorithms, and tooling using Python and SQL to support real-time data ingestion and model scoring for the KYC/KYB process.
• Collaborate with analysts, compliance teams, and engineers to translate complex business and regulatory requirements into actionable data insights and automated solutions.
• Stay informed about the latest advancements in data science, machine learning, and regulatory compliance techniques to ensure state-of-the-art capabilities in the risk domain.

• Proven experience in a Data Scientist role, ideally with exposure to fraud detection, anti-money laundering (AML), or KYC/KYB domains within a Fin Tech or regulated business environment.
• Strong proficiency in machine learning frameworks and Python programming language and are able to make and justify design decisions in your code. You know how to use Git to collaborate with others and are able to review code.
• Expertise in data querying languages such as SQL, with experience working with large datasets and data processing technologies (e.g., Spark, Snowflake).
• Familiarity with anomaly detection, supervised and unsupervised learning methods, and real-time risk scoring and data analysis.
• Demonstrated ability to work collaboratively in cross-functional teams and effectively communicate complex technical concepts to non-technical stakeholders.
• A strong product mindset with the ability to work independently in a cross-functional and cross-team environment.
• Experience with statistical analysis and good presentation skills to drive insight into action.
• Strong problem‑solving skills with the ability to help refine problem statements and figure out how to solve them.
• Familiarity with automating operational processes via technical solutions, for example Large Language Models (LLMs)

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.

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