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Tier-1 brand, popular Data Scientist role, metro location, mid-level experience increase competition.
Strong banking and credit/fraud domain requirements reduce cross-industry transferability.
Requires banking credit/fraud domain experience plus ML lifecycle and advanced Python/PySpark skills.
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Develop and deploy machine learning prediction models across customer lifecycle stages including Acquisition, Behaviour, Collections, Marketing, and Fraud.
Lead large-scale projects involving model development and deployment using big data from digital signals and alternate data sources.
Design strategies to offer best-in-class asset products by partnering with data providers and expanding solution capabilities.
Advanced degree in Data Science, Computer Science, Engineering, Applied Mathematics, Statistics or substantial relevant hands-on experience.
Experience in Banking/Fintech domain specifically in Credit/Fraud Risk with strong understanding of Bureau Data and internal banking data.
Proficiency in Python, SQL, or Pyspark with strong knowledge of the full Machine Learning lifecycle including feature engineering, training, validation, deployment, and monitoring.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in end-to-end machine learning model design, development, validation, documentation, and deployment in financial services.
Strong analytical and statistical inference capabilities with expertise in exploratory data analysis and creating compelling data stories.
Able to manage and own complex large-scale data science projects involving multiple stakeholders and data partners.