Lead Data Scientist - Fraud Prevention

About the Role: 

The 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.

 

Here’s how you’ll 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: Maintain and optimize existing risk models to ensure their accuracy and reliability.

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

Innovate and Develop: Lead the development and deployment of machine learning models, features and help deploy intelligence to production.

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 Fraud Risk Team's strategies.

Collaboration & Communication: Work closely with the Fraud Risk Team to understand business processes and risk factors.

Communicate complex data findings and insights effectively to non-technical stakeholders.

Risk Reduction Initiatives: Identify opportunities to reduce the impact of risks on good customers through data-driven 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;

• Solid knowledge of Python, and ability 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;

• Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL);

• 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 with MLOps tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CD;

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

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.