Data Scientist - Fraud Analytics

The position is in the Fraud & Operational Analytics Team. This team is mainly responsible for creating Machine Learning/ AI solutions for effective fraud prevention and driving operational scalability to improve customer experience, reduce operational cost, risk mitigation, etc. The role will focus on building cutting-edge ML solutions.

Responsibilities :

- Analyze large amounts of data to derive business insights and create innovative solutions.

- Understanding business nuances and associated fraud patterns.

- Develop advanced ML models for fraud identification.

- Innovate with a focus on better and newer approaches.

- Explore alternate data sources that can add value on top of traditional data sources.

- The role requires exhibiting a high level of expertise in data strategy and model training pipelines.

- She/He will drive insights in generating data-driven, actionable strategies.

- Supporting the business and risk teams with bespoke and strategic analysis.

Requirements :

- Proficiency and experience in econometric, statistical, and machine learning techniques.

- Proficiency in Python, SQL, Pandas, Numpy, Sklearn, Tensorflow/PyTorch, etc.

- Strong understanding of statistical concepts and modeling techniques for regression, classification, and anomaly detection.

- Good understanding of evaluation metrics.

- Logical thought process, ability to scope out an open-ended problem into data data-driven solution.

- Engg, MBA, Master of Economics, Master of Statistics from Top Tier colleges.

- 3-4 years of experience in Fintech, Banking Domain.

- Worked on Anomaly Detection and unbalanced datasets previously.

- Good understanding of Machine learning systems and Machine learning pipelines.