Lead Data Scientist - Scams Prevention

Posted Yesterday
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London, Greater London, England, GBR
Hybrid
Entry level
Fintech • Mobile • Payments • Software • Financial Services
Wise is one of the fastest growing fintechs in the world and we’re on a mission to make money without borders a new norm
The Role
Lead development and deployment of machine learning models that detect account takeover and send scams. Analyze transaction and event-log data, build real-time detection pipelines, design experiments, identify fraud patterns, and collaborate with engineering, security, and product teams. The role also involves mentoring junior data scientists, communicating insights to nontechnical stakeholders, and applying methods including anomaly detection, supervised and unsupervised learning, deep learning, graph-based solutions, and statistical analysis.
Summary Generated by Built In
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.

Job Description

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.

 

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.

 

Qualifications

A bit about you: 

  • 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;
    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.


 

Additional Information

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.

Skills Required

  • Experience in data science focused on fraud detection, cybersecurity, or fintech
  • Experience building machine learning models for send scam victim identification and account takeover detection
  • Proficiency with Python, R, or similar programming languages
  • Experience with large datasets and Hadoop, Spark, SQL, or similar data-processing technologies
  • Familiarity with anomaly detection, supervised learning, unsupervised learning, deep learning, and graph-based solutions
  • Ability to collaborate cross-functionally and communicate complex technical concepts to nontechnical stakeholders
  • Strong Python knowledge, including making and justifying code design decisions
  • Experience using Git and GitHub pull requests, reviewing code, and reading Java code
  • Experience collaborating with engineering teams on services
  • Experience mining event logs to identify patterns and associations
  • Knowledge of gradient boosting, neural networks, regression, autoencoders, clustering, and model selection
  • Experience with statistical analysis and presenting insights
  • Strong product mindset and ability to work independently in cross-functional teams
  • Strong problem-solving and problem-definition skills

What the Team is Saying

Wise Compensation & Benefits Highlights

  • Leave & Time Off Breadth — U.S. materials describe extensive paid leave (PTO, sick time, holidays, “Me Days,” volunteer and compassionate leave) plus a paid six‑week sabbatical after four years. Feedback suggests the time‑off package is a standout element of the offering.
  • Parental & Family Support — Benefits information highlights up to 18 weeks of fully paid parental leave in many locations, with clear tenure qualifiers in some markets. Feedback suggests these policies are robust and a notable strength.
  • Equity Value & Accessibility — Company and job-posting materials indicate RSUs are a regular part of compensation, with a shift toward RSUs since 2022. Feedback suggests the equity component is a relatively substantial part of total rewards.

Wise Insights

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The Company
9,000 Employees
Year Founded: 2011

What We Do

Wise is a global technology company, building the best way to move and manage the world's money. With Wise Account and Wise Business, people and businesses can hold 40 currencies, move money between countries and spend money abroad. Large companies and banks use Wise technology too; an entirely new network for the world's money. Launched in 2011, Wise is one of the world’s fastest growing, profitable tech companies. In fiscal year 2025, Wise supported around 15.6 million people and businesses, processing over $185 billion in cross-border transactions and saving customers around $2.6 billion.

Why Work With Us

We’re truly global in who we are, how we work, and how we build. Everything we do is centred around creating a world of money that’s fast, easy, fair. And open to all. Everyone who works here owns a piece of Wise, from the work they do, to the stock they hold.

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Wise Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

We expect new joiners in the office most days to build connections and learn from colleagues for their first six months. After that, most Wisers split their working week between the office and home, typically coming in at least 12 times a month.

Typical time on-site: Flexible
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