Data Scientist (Fraud & Ops Analytics)

The position is in the Fraud and 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:

• Analyse 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 modelling techniques for regression, classification, and anomaly detection.
• Good understanding of evaluation metrics.
• Logical thought process and ability to scope out an open-ended problem into a data-driven solution.
• MBA, Master of Economics, and Master of Statistics from top-tier colleges.
• 3-4 years of experience in the fintech and banking domains.
• Worked on anomaly detection and unbalanced datasets previously.
• Good understanding of machine learning systems and machine learning pipelines.