Successful candidates need to be comfortable with working on weekends, and rotation shifts as and when required.

Key Responsibilities

• Design and implement advanced data science solutions across banking domains, with a focus on automation and integration with enterprise database platforms.
• Lead the automation of end-to-end data science workflows, including data extraction, transformation, modelling, and deployment.
• Work closely with database engineering teams to ensure seamless integration between models and large-scale relational and non-relational databases.
• Champion the development of reusable, scalable ML pipelines for model training, evaluation, monitoring, and retraining.
• Drive AI/ML adoption for use cases like fraud detection, risk scoring, operational efficiency, customer analytics, and more.
• Contribute to data governance and model risk management processes in line with regulatory standards. ...