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Tier-1 brand, metro location, mid-level data scientist title, and generic demand amplify competition.
Strong banking, credit, fraud and bureau-data requirements create high domain specificity and low transferability.
Requires banking credit/fraud domain expertise plus mandatory Python/PySpark and end-to-end ML experience, so filters are strict.
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Develop and deploy predictive machine learning models across the entire customer lifecycle including acquisition, behavior, collections, propensity, marketing, and fraud.
Lead large-scale projects involving model development, deployment, and management using big data from digital signals and alternative data sources.
Design strategy and partner with data providers and companies to enhance asset product offerings.
Advanced degree in Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, or demonstrated substantial hands-on experience in the field.
Experience in Banking or Fintech domain specifically in Credit or Fraud Risk with strong knowledge of Bureau Data and in-house banking data.
Proficient coding skills in at least one: Python, SQL, or Pyspark.
Strong understanding and hands-on experience in the complete ML lifecycle: feature engineering, training, validation, deployment, scoring, monitoring, and feedback loop.
Experienced in end-to-end ML model design, development, validation, documentation, and deployment in banking or fintech settings.
Able to manage and lead multiple large-scale predictive modeling projects involving integration of diverse data sources.
Skilled in robust exploratory data analysis (EDA) and statistical inference to create compelling, data-driven business stories.