Lead Data Scientist - Scams Prevention

Wise is a global technology company building the best way to move and manage the world’s money. The company is on a mission to make international payments easier and more affordable, helping people and businesses send money abroad, spend internationally, and make and receive payments.

Wise is looking for a Lead Data Scientist to join its growing Scams Prevention team. This role offers the opportunity to work behind the scenes of company transactions, understand how risk is mitigated, and help provide customers with a seamless service. The work will have a direct impact on Wise’s mission and millions of customers.

As a Lead Data Scientist in the Scams Prevention team, you will use your data science expertise to innovate and deploy models that detect and prevent fraudulent activity. Your work will help safeguard the platform against unauthorised access and enhance Wise’s security framework. You will collaborate closely with engineering, product and security operations teams.

Key responsibilities include leading the development and deployment of advanced machine learning models to detect, predict and mitigate account takeover attempts; analysing large volumes of data to identify trends, patterns and anomalies associated with account takeover and Send Scam threats; designing and implementing experiments to evaluate and improve fraud detection systems; collaborating with security analysts and engineers to translate business and security requirements into actionable data insights and solutions; and developing robust data pipelines, algorithms and tools to support real-time detection and response.

You will stay informed about advances in data science, machine learning and fraud prevention techniques, and mentor and guide junior data scientists while fostering collaboration and continuous learning.

The role requires proven data science experience focused on fraud detection, cybersecurity or fintech, including experience building machine learning models for Send Scam victim identification and account takeover. You should have strong proficiency in machine learning frameworks and programming languages such as Python or R; experience with large datasets and technologies such as Hadoop, Spark and SQL; and familiarity with anomaly detection, supervised and unsupervised learning, deep learning and graph-based solutions.

You should be able to collaborate effectively in cross-functional teams, communicate complex technical concepts to non-technical stakeholders, and apply a proactive problem-solving mindset to protecting users from fraudulent activity. The role also requires solid Python knowledge, the ability to make and justify design decisions, experience using Git and reviewing code, the ability to read Java code, and demonstrable experience collaborating with engineering teams on services. Experience mining event logs to identify patterns and associations is also required, together with familiarity with gradient boosting, neural networks, regression, autoencoders, clustering and combinations of these approaches.

You will need experience with statistical analysis, good presentation skills, a strong product mindset, the ability to work independently across teams, good communication skills, and the ability to refine problem statements and determine how to solve them.

Wise wants to work with the best people regardless of background and is committed to building diverse, equitable and inclusive teams. The company describes itself as an international team that values difference and enables every Wiser to feel respected, empowered to contribute and able to progress in their career.