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 DescriptionAbout the Role
We’re looking for a Senior Product Manager to join Wise’s Fraud Prevention team - a group dedicated to making it impossible for criminals to use Wise for illicit activity while ensuring legitimate customers can move money without unnecessary friction.
This role is all about designing, delivering, and scaling fraud controls that stop bad actors in their tracks. You’ll work closely with engineers, analysts, data scientists, and fraud operations specialists to disrupt fraud before it impacts our customers or the business.
In this role, you will be responsible for driving down the volume of Wise accounts being used for malicious transactions - including preventing Scams, Chargebacks and First Party Fraud.
Your Mission
Own the strategy for detecting and preventing high-impact fraud typologies, including inbound scam and funding fraud.
Develop and deliver intelligence-led fraud controls that block illicit activity in real time.
Ensure controls balance fraud prevention with a great experience for genuine customers.
Partner with Fraud Operations to design scalable operational processes and tooling.
Measure impact and continuously iterate based on data and emerging threats.
What You’ll Work On
Building detection models, rules, and signals to identify suspicious activity.
Designing proactive measures that stop fraud before it happens.
Enhancing Wise’s ability to respond to emerging threats at speed.
Creating feedback loops between operational findings and product improvements.
Partnering with data scientists to shape model requirements and evaluate performance.
Driving the integration of AI/ML detection with customer-facing fraud prevention flows.
Exploring innovative uses of data and automation to disrupt financial crime at scale.
Qualifications
What We’re Looking For
Proven experience in fraud prevention, AML, risk management, or related financial crime areas.
Strong analytical skills and comfort working with large datasets to size problems and track outcomes.
Experience building products or controls in partnership with engineering, data science and design.
An understanding of how fraudsters operate and adapt their methods.
Strong understanding of how AI/ML models are developed, deployed, and monitored, with awareness of precision/recall trade-offs and model lifecycle.
Ability to design product features that generate high-quality data signals to improve model effectiveness.
Awareness of emerging fraud prevention technologies (biometrics, behavioural analytics, network intelligence) and ability to evaluate them critically.
Excellent problem-solving, communication, and stakeholder management skills.
Why You Should Join Wise
🌍 Impact: Your work will protect millions of customers globally, preventing scams and financial crime.
🚀 Autonomy: You’ll own your roadmap, working in an empowered, fast-moving team.
📊 Data at Scale: Work with real-world fraud and scam patterns, leveraging AI and behavioral insights.
🌱 Growth: Be part of a company that’s expanding rapidly, offering huge career development opportunities.
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.
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