Machine Learning Scientist - Fraud

Company Description

Wise is one of the fastest growing companies in Europe and we’re on a mission: to make money without borders the new normal. We’ve got 13 million customers across the globe and we’re growing. Fast.

Current banking systems don't let us send, spend or receive money across borders easily. Or quickly. Or cheaply. 

So, we’re building a new one.

Job Description

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 Machine Learning Scientist to join our 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 and prevent them from being victims of crime on the platform. 

Key Responsibilities:

Model Maintenance and Improvement:

Maintain optimize existing risk models to ensure their accuracy and reliability.
Continuously monitor model performance and implement improvements based on feedback and testing.

Feature & Model Development:

Develop and implement new features to enhance model performance and risk prediction capabilities.
Collaborate with cross-functional teams to identify and integrate relevant data sources for better risk assessment.
You will help the data science team develop models for anomaly detection through prototyping model features and develop them into production ready pipelines

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.

Collaboration & Communication:

Work closely with the 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.

• You have a solid knowledge of Python, and are able 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;

• You have experience with mining into event logs to identify patterns and associations

• You are familiar with a range of model types, and know when and why to use gradient boosting, neural networks, regression, autoencoders, clustering or a blend of these 

• 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):

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

• Experience on working with non supervised algorithms

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

Salary for this role is £65,000 - £85,000 + RSU's

Additional Information

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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