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 DescriptionThe Fraud team at Wise is dedicated to safeguarding our platform against financial crime and ensuring the protection of our legitimate customers. Leveraging cutting-edge machine learning, real-time transaction monitoring, and data analysis, our team is responsible for developing and enhancing fraud detection systems. Software engineers, data analysts, and data scientists collaborate on a daily basis to continuously improve our systems and provide support to our fraud investigation team.
Our vision is:
Build a globally scalable fraud prevention and detection engine to maintain Wise as a secure environment for our legitimate customers.
Utilise machine learning techniques to identify potential risks associated with customer activity.
Foster a strong partnership between our fraud investigators and the product team to develop solutions that leverage the expertise of fraud prevention specialists.
Not only meet the requirements set by regulators and auditors but also surpass their expectations.
We are looking for a highly skilled Staff Data Scientist to lead technical innovation and drive the development of advanced data science solutions. This role is pivotal in enhancing our fraud detection capabilities and ensuring the security of our platform.
Here’s how you’ll be contributing:
Innovate and Develop: Lead the development and deployment of machine learning models, including neural networks, anomaly detection, graph-based models, Transformers.
Lead and Collaborate: Mentor team members and promote adoption of AI workflows for automation across the business. Collaborate with cross-functional teams to integrate data science solutions into Fraud prevention product offerings.
Deploy and Integrate: Develop scalable deployment strategies together with Platform teams and integrate LLMs with AI agents for seamless production use.
Optimise and Evaluate: Conduct large-scale training and hyper-parameter tuning, and define performance metrics to ensure high-quality model outputs.
Data Strategy and Management: Design and implement strategies for data collection, curation, and augmentation to support robust model training.
Documentation and Reporting: Communicate complex data findings to non-technical stakeholders effectively. Document the development and maintenance processes for models and features.
Qualifications
Demonstrated expertise (5+ years) in developing and deploying production-grade AI systems and Machine Learning (ML) in the fraud or risk domain.
Technical Proficiency: Skilled in Python, capable of delivering production-ready Python services as required; possesses hands-on experience with neural networks and deep learning models; has comprehensive knowledge of machine learning frameworks such as TensorFlow or PyTorch, as well as AI agent frameworks like LLamaIndex and LangGraph; well-versed in LLM orchestration and MCP usage.
Data-driven mindset: skilled in designing data strategies, including data collection, curation, and augmentation, to support model development. Experience with big-data frameworks and working with large scale databases
Technical leadership and mentorship: demonstrated ability to guide, mentor and level-up teams on technical aspects, fostering a collaborative and innovative work environment
Excellent communication skills, capable of simplifying complex technical concepts for easy understanding; able to adapt communication style to suit different audiences; can effectively engage and advise both technical and non-technical stakeholders with clarity and logic
A strong product mindset with the ability to work independently in a cross-functional and cross-team environment;
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.
Similar Jobs
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.
Gallery
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.




.png)





.png)
