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Tier-1 brand, popular Data Scientist title, mid-level range, and metro Bangalore drive high competition.
Requires deep banking credit and bureau-data expertise, so background fit is highly industry-specific.
Requires domain-specific banking credit/fraud experience, ML model lifecycle, and specific Python/PySpark skills.
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Develop predictive models using machine learning across the entire customer lifecycle including acquisition, behavior, collections, marketing, and fraud detection.
Lead and manage large-scale projects for model development and deployment focusing on credit and fraud risk within digital banking.
Collaborate with data providers and companies to enhance solutions using big data from digital signals and alternative data sources.
Advanced degree in Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, or substantial hands-on experience in the field.
Experience in Banking/Fintech domain specifically in Credit/Fraud Risk with strong knowledge of Bureau and in-house banking data.
Proficiency in Python, SQL, or Pyspark with strong coding skills.
Deep understanding of the machine learning lifecycle including feature engineering, training, validation, deployment, and monitoring.
Hands-on experience owning end-to-end machine learning model lifecycle including design, development, validation, documentation, and deployment.
Experience working with large-scale digital data sets and building solutions in a fintech or banking environment focused on credit or fraud risk.
Ability to partner cross-functionally with external data providers and internal teams to integrate advanced analytics solutions.