Associate Data Scientist
Bureau, Bangalore
What you will do
- Build and maintain features and components for graph-native models (GNNs, embeddings, graph algorithms) used to detect fraud, collusion, and identity risk
- Support the design, training, and evaluation of statistical, AI/ML, and deep learning models for fraud and risk, under a senior data scientist's guidance
- Run experiments, clean and prepare data, and do light pipeline work to help tune model performance
- Write production-quality code backed by statistics and ML, and help build out ML observability for the models you work on
- Document your work clearly enough that others on the team can pick it up
What you need
- BS/MS in ML, CS, Math, Stats, or a related field; 1 to 3 years in DS/AI/ML Engineering roles (internships with real shipped work count)
- Strong Python; working knowledge of SQL; exposure to a cloud platform (AWS/Databricks/Azure) is a plus
- Some experience taking a model from a notebook toward something production-adjacent, ideally with fraud, risk, payments, or compliance data — but we'll take strong fundamentals over domain experience