Data Scientist

Overview

As a Data Scientist, you will build graph-native models and support statistical, AI/ML, and deep learning models used to detect fraud, collusion, and identity risk.

What you'll do

• Build and maintain features and components for graph-native models, including GNNs, embeddings, and 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.
• 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'll 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.
• Some experience taking a model from a notebook toward something production-adjacent, ideally with fraud, risk, payments, or compliance data.
• Comfort with ambiguity and unlabelled data.
• Initiative to ask questions and look things up rather than wait to be told.

Nice to have

• Exposure to a cloud platform such as AWS, Databricks, or Azure.
• Prior exposure to graph-based thinking, including coursework or personal projects.

Details

• Location: Bangalore.