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Tier-1 brand, generic Data Scientist title, metro location, and broad ML/banking skill requirements increase competition.
Requires banking credit/fraud domain knowledge and bureau data expertise, limiting cross-industry transferability.
Mandatory banking/credit fraud domain experience and required ML coding skills make filters highly strict.
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Develop and deploy predictive models across the customer journey including acquisition, behavior analysis, collections, marketing, and fraud detection using ML algorithms.
Design strategies to offer optimized asset products leveraging big data from digital signals and alternative data sources.
Lead and manage large-scale model development projects and collaborate with external data providers and partners.
Advanced degree in Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, or equivalent hands-on experience.
Experience in Banking or Fintech domain focused on Credit or Fraud Risk with strong knowledge of Bureau and in-house banking data.
Proficiency in Python, SQL, or Pyspark with strong understanding of end-to-end ML lifecycle including feature engineering, training, validation, deployment, and monitoring.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in managing full lifecycle of predictive models specifically in financial services or banking domains.
Strong technical expertise in machine learning model deployment and monitoring with large-scale data from diverse sources.
Capable of strategic collaboration with multiple stakeholders including data providers and commercial partners.