Lead Data Scientist - Scams Prevention

Skills this role screens for

About the role

We’re looking for a Lead Data Scientist to join our growing Scams Prevention team.

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.

As a Lead Data Scientist in the Scam Prevention team, you will leverage your expertise in data science to innovate and deploy models that detect and prevent fraudulent activities. Your work will directly influence our ability to safeguard our platform against unauthorised access and enhance our overall security framework. You will collaborate closely with cross-functional teams, including engineering, product, and security operations.

The work

Key Responsibilities

• Lead the development and deployment of advanced machine learning models to detect, predict, and mitigate account takeover attempts.
• Analyse large volumes of data to identify trends, patterns, and anomalies associated with potential ATO and Send Scam threats.
• Design and implement experiments to evaluate the effectiveness of fraud detection systems and continuously improve their performance.
• Collaborate with security analysts and engineers to translate business and security requirements into actionable data insights and solutions.
• Develop robust data pipelines, algorithms, and tools to support real-time detection and response to ATO and Send Scam threats.
• Stay informed about the latest advancements in data science, machine learning, and fraud prevention techniques to ensure state-of-the-art capabilities in ATO and Send Scam.
• Mentor and guide junior data scientists, fostering a culture of collaboration and continuous learning within the team.

Who they want

A bit about you

A strong product mindset with the ability to work independently in a cross-functional and cross-team environment;

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.

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.Jobs.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.

• Proven experience in a data science role with a focus on fraud detection, cybersecurity, or fin tech related domains
• Have build machine learning models for Send Scam (Victim Identification) and Account Takeover (ATO)
• Strong proficiency in machine learning frameworks and programming languages such as Python, R, or similar.
• Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL).
• Familiarity with anomaly detection, supervised, unsupervised learning methods, deep learning, and graph based solutions
• Demonstrated ability to work collaboratively in cross-functional teams and effectively communicate complex technical concepts to non-technical stakeholders.
• A proactive, problem-solving mindset with a passion for protecting users from fraudulent activities.
• You have a solid knowledge of Python, and are able 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;
• You have experience with mining into event logs to identify patterns and associations
• You are familiar with a range of model types, and know when and why to use gradient boosting, neural networks, regression, autoencoders, clustering or a blend of these
• Experience with statistical analysis and good presentation skills to drive insight into action;
• 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.