Opportunity at
WiseLead Data Scientist - Scams Prevention
Wise is recruiting a Lead Data Scientist for its Scams Prevention team in London. The work sits behind the scenes of customer transactions, aiming to identify…
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About This Role
Wise is recruiting a Lead Data Scientist for its Scams Prevention team in London. The work sits behind the scenes of customer transactions, aiming to identify and stop account takeover and send-scam activity while keeping the service smooth for customers. The team's models feed into Wise's wider security framework and affect a very large customer base. The lead builds and releases machine learning models that detect, predict and reduce account…
Job Description
Wise is recruiting a Lead Data Scientist for its Scams Prevention team in London. The work sits behind the scenes of customer transactions, aiming to identify and stop account takeover and send-scam activity while keeping the service smooth for customers. The team's models feed into Wise's wider security framework and affect a very large customer base.
The lead builds and releases machine learning models that detect, predict and reduce account takeover attempts and send scams. Day-to-day work includes analysing large volumes of data for trends and anomalies, designing experiments to test how well fraud detection performs, and building data pipelines, algorithms and tools that make instant detection and response possible.
The role works closely with security analysts, engineers and product colleagues to turn business and security needs into data-driven solutions. The lead also keeps up with new methods in statistical modelling and fraud-prevention practice, and mentors junior data scientists to build a collaborative, learning-focused team.
The posting asks for a candidate with a product mindset who can work independently across teams. The role is on-site in London.
Responsibilities
- Lead the building and release of machine learning models for account takeover and send-scam detection.
- Analyse large datasets to find trends, patterns and anomalies linked to these threats.
- Design and run experiments that measure and improve fraud detection performance.
- Translate requirements from security and engineering colleagues into data solutions.
- Build pipelines, algorithms and tools that enable instant detection and response.
- Mentor junior data scientists and share knowledge across the team.
Requirements
- A track record as a data scientist working on fraud detection, cybersecurity or fintech problems.
- Previous experience building models that spot account takeover and victims of send scams.
- Strong skills in Python, R or similar, and in working with large datasets using SQL, Hadoop or Spark.
- Knowledge of anomaly detection, supervised and unsupervised learning, deep learning and graph-based methods.
- Ability to justify design choices in code, use Git and GitHub pull requests, and review code, including Java.
- Experience working with engineering teams on services and mining event logs for patterns.
- Comfort with several model families, such as tree-based boosting, neural nets, regression, autoencoders and clustering, and knowing when each fits.
- Statistical analysis skills and the ability to present insights and explain complex concepts simply.
Core Skills
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Where is the role based?
The role is based in London and is listed as on-site.
What kind of models does the role build?
The work covers machine learning models that detect account takeover and send-scam activity, including victim identification.
Is a salary amount listed?
The posting does not specify a salary.
Is the role full-time?
Yes, it is listed as a full-time position.
Sample Interview Questions
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