Lead Fraud Prevention Data Scientist

Were looking for a Data Scientist to join our growing Fraud Decisioning Team in London. 

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 Wises mission and millions of our customers.

The Fraud team at Wise is dedicated to safeguarding our platform against financial crime and ensuring the protection of our legitimate customers. Leveraging cuttingedge machine learning realtime 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 Data Scientist to join our Receive Risk Team. In this role you will 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 Receive Risk Team to support the effective management and mitigation of risks associated with our receiving processes.

Key Responsibilities:

Model Maintenance and Improvement:

Maintain and optimize existing risk models to ensure their accuracy and reliability.

Continuously monitor model performance and implement improvements based on feedback and testing.

Feature Development:

Develop and implement new features to enhance model performance and risk prediction capabilities.

Collaborate with crossfunctional teams to identify and integrate relevant data sources for better risk assessment.

Data Analysis & Intelligence Creation:

Conduct thorough data analysis to identify trends patterns and anomalies that can aid in risk mitigation.

Develop actionable intelligence and insights to inform the Receive Risk Teams strategies.

Collaboration & Communication:

Work closely with the Receive Risk Team to understand business processes and risk factors.

Communicate complex data findings and insights effectively to nontechnical stakeholders.

Risk Reduction Initiatives:

Identify opportunities to reduce the impact of risks on good customers through datadriven strategies and interventions.

Develop and test strategies to balance risk mitigation with customer satisfaction.

Documentation & Reporting:Document the development and maintenance processes for models and features.

Prepare and present detailed reports and dashboards that reflect risk assessment outcomes and model performance.
 

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 crossfunctional and crossteam environment;

• Good communication skills and ability to get the point across to nontechnical 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):  

• Familiarity with automating operational processes through technical solution for example Large Language Models;

• Experience on working with non supervised algorithms

• Prior experience in the fraud domain and a strong understanding of fraud detection techniques.

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 Wise.Jobs.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.

Remote Work :

No

Employment Type :

Fulltime