About the role
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.