Machine Learning Engineer, Fraud Prevention

Vivid Money is an innovative FinTech start-up that offers a unique mobile banking app at your fingertips. We offer our financial product to Personal and Business customers. Whether it's payments, transfers, multi-currency accounts for your travels, spendings reports, bills splitting — with Vivid, managing all your finances and investing your money is easy, flexible, and 100% transparent.

About the team

The team plays a key role for overseeing and managing functions and processes within the Financial Crime Prevention. Our goal is to provide exceptional customer service, resolve complex issues, and ensuring proper execution of compliance policies, prevent exposure to ML/TF and fraud risks while support a smooth customer experience with minimal disturbance.

Responsibilities

• Design, develop, and launch machine learning models to identify fraudulent patterns and behaviors in real-time.
• Collaborate with cross-functional teams to integrate ML solutions into fraud detection systems.
• Monitor model performance and implement updates to improve accuracy and adaptability.
• Build and maintain scalable, efficient ML pipelines and infrastructure.
• Propose new features and ideas in order to improve top line metrics.
• Work with large-scale, complex datasets to extract meaningful insights and features for model training.
• Collaborate with stakeholders and drive end-to-end projects involving a variety of technologies and systems to successful completion.
• Stay up-to-date with the latest research and advancements in fraud detection and machine learning.

• 3+ years proven industry experience in machine learning, data science, and statistical modeling, ideally in fraud prevention or financial services.
• Strong proficiency in Python, SQL, and relevant ML libraries such as TensorFlow, Scikit-learn, or PyTorch.
• Experience with feature engineering, model tuning, and validation techniques.
• Knowledge of anomaly detection, classification algorithms, and fraud detection strategies.
• Experience working with cloud environments (e.g., AWS, Azure).
• Problem-solving skills with attention to details and a proactive approach to solving complex problems.

Nice to Have:

• An advanced degree in a quantitative field (e.g. stats, physics, computer science)
• Experience in fintech or fraud prevention domain.
• Experience in software development field (Java/Kotlin/Golang/Python)
• Familiarity with real-time data processing and streaming technologies (e.g., Kafka).
• Understanding of regulatory and compliance considerations in fraud prevention.