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Tier-1 brand, mid-level data scientist demand, and Bengaluru location increase competition density.
Strong banking and credit/fraud domain expertise reduces cross-industry transferability.
Requires banking credit/fraud domain experience and mandatory ML coding and deployment skills.
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Develop and deploy predictive machine learning models across the customer lifecycle including acquisition, behavior, collections, marketing, and fraud.
Design asset product strategies leveraging big data from digital signals and alternative data sources.
Lead and manage large scale projects involving model development and deployment, collaborating with data providers and partners.
Advanced degree in Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, or equivalent hands-on experience.
Prior experience in Banking or FinTech domain focused on Credit or Fraud Risk, with strong understanding of Bureau Data and banking data.
Proficiency in at least one coding language: Python, SQL, or PySpark.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end machine learning lifecycle including feature engineering, model training, validation, deployment, and monitoring.
Strong analytical skills demonstrated through exploratory data analysis and statistical inference for actionable data storytelling.
Able to lead cross-functional projects involving data partnerships and large-scale model implementations in digital banking environments.