About the Company

We are a technology-driven financial services company developing secure digital payment and financial products for businesses and consumers across Europe. Our teams use advanced analytics, automation, and machine learning to identify suspicious activity, protect customers, and strengthen the integrity of digital financial transactions.

Role Overview

We are seeking a Machine Learning Engineer specialising in fraud detection to design, develop, and deploy intelligent models that identify fraudulent transactions and suspicious financial behaviour. You will work with data scientists, software engineers, fraud analysts, and risk specialists to build scalable machine learning solutions that improve fraud detection accuracy while minimising false positives and unnecessary transaction declines.

Key Responsibilities

• Design, train, validate, and deploy machine learning models for payment fraud detection and suspicious activity identification.
• Analyse transaction data, behavioural patterns, device information, and customer activity to identify potential fraud indicators.
• Develop real-time fraud scoring services that integrate with payment processing and risk management systems.
• Build feature engineering pipelines using structured transaction data and behavioural signals.
• Evaluate model performance using precision, recall, F1-score, ROC-AUC, and fraud-specific business metrics.
• Monitor false-positive rates, fraud losses, model drift, and changes in transaction patterns.
• Implement model monitoring, retraining workflows, and performance alerts.
• Collaborate with fraud operations teams to incorporate investigation outcomes and confirmed fraud cases into model development.
• Ensure models are explainable, appropriately governed, and designed to support applicable financial regulations and data protection requirements.
• Maintain technical documentation and contribute to machine learning deployment standards.

Requirements and Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field, or equivalent practical experience.
• At least 3 years of experience in machine learning, data science, or applied predictive modelling.
• Strong Python programming skills and experience with libraries such as scikit-learn, XGBoost, LightGBM, or PyTorch.
• Experience developing classification models, anomaly detection systems, and fraud prediction solutions.
• Strong understanding of feature engineering, imbalanced datasets, model validation, and statistical evaluation.
• Experience working with SQL, large datasets, and data processing frameworks.
• Familiarity with MLflow, Docker, cloud platforms, CI/CD, or model-serving infrastructure.
• Understanding of payment fraud, account takeover, transaction monitoring, or financial crime patterns is an advantage.
• Knowledge of explainable AI, GDPR, and responsible machine learning practices is desirable.
• Strong analytical thinking and communication skills.

What We Offer

• Competitive salary based on experience and technical expertise.
• Flexible hybrid or remote working arrangements, subject to company policy.
• Opportunity to develop production-grade fraud detection systems.
• Access to modern data infrastructure and machine learning technologies.
• Professional development, technical training, and knowledge-sharing opportunities.
• Collaborative work with experienced engineers, data scientists, and financial risk professionals.

Application Instructions

Submit an up-to-date CV outlining your machine learning experience, relevant technical skills, and examples of predictive models or fraud detection solutions you have developed. Shortlisted candidates may be invited to discuss a technical project or complete a machine learning assessment.

Help build intelligent systems that detect fraud earlier, protect customers, and strengthen trust in digital payments.

Are you interested in this position?

Apply by clicking on the “Apply Now” Button below!

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