Employer Active
Job Alert
You will be updated with latest job alerts via emailJob Alert
You will be updated with latest job alerts via emailThe 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 someone who will help maintain our existing machine learning algorithms while helping to make them better and develop new intelligence to stop fraudsters.
Heres how youll be contributing:
We are seeking a highly motivated Lead Data Scientist to join our Fraud Risk Team. In this role you will level up the intelligence and maintain and refine existing models develop new features and create new intelligence to reduce the impact on good customers. You will work closely with the Fraud Risk Team to support the effective management and mitigation of risks associated with our receiving processes. Further you will help grow our data science team in space.
Key Responsibilities:
Model Maintenance and Improvement:
Innovate and Develop:
Data Analysis & Intelligence Creation:
Collaboration & Communication:
Risk Reduction Initiatives:
Documentation & Reporting:
A bit about you:
Proven track record of deploying models from scratch including data preprocessing feature engineering model selection evaluation and monitoring.
Strong Python knowledge. Ability to read through code especially Java. Demonstrable experience collaborating with engineering on services;
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.
Some extra skills that are great (but not essential):
Experience on working with non supervised algorithms
Prior experience in the fraud domain and a strong understanding of fraud detection techniques.
Were people without borders without judgement or prejudice too. We want to work with the best people no matter their background. So if youre passionate about learning new things and keen to join our mission youll fit right in.
Also qualifications arent that important to us. If youve got great experience and youre great at articulating your thinking wed like to hear from you.
And because we believe that diverse teams build better products wed especially love to hear from you if youre from an under-represented demographic.
Additional Information :
For everyone everywhere. Were people building money without borders without judgement or prejudice too. We believe teams are strongest when they are diverse equitable and inclusive.
Were 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 its like to work at Wise visit .
Keep up to date with life at Wise by following us on LinkedIn and Instagram.
Remote Work :
No
Employment Type :
Full-time
Full-time