Machine Learning Engineer for Fraud Detection
Skills Required
Description
Fraud detection has become one of the most critical areas of our platform, and we need an engineer who can design machine learning models capable of identifying suspicious activity with a high degree of accuracy.
The role involves building, training, and testing models that use behavioral data and transaction histories to flag potentially fraudulent patterns before they cause damage. It’s not just about accuracy in isolation but also about precision and recall—false positives must be minimized without missing real threats.
Python will be the core language for this work, alongside libraries such as Scikit-learn, PyTorch, and Pandas. The engineer should feel comfortable moving between data preprocessing, feature engineering, and advanced modeling techniques.
We expect a strong foundation in model evaluation and validation. The fraud detection system will only be valuable if it consistently performs under real-world conditions, so robustness and reliability will matter just as much as innovation.
The project will also require integration with SQL databases, as much of the data pipeline depends on structured queries and efficient data retrieval. Strong SQL skills are essential for managing and cleaning the datasets that will feed the models.
This is not a role for someone who wants to experiment endlessly without delivering results. We need practical models that can be deployed and iterated on as feedback and new data become available....