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Popular mid-level Data Scientist role with broad ML/Python/SQL requirements and 5–8 year band.
Requires finance-specific domain knowledge and model risk/regulatory experience, reducing cross-industry transferability.
Explicit 5–8 year range, mandatory ML/modeling skills, Python/Spark/SQL and regulated model governance raise screening strictness.
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Independently perform advanced analytics including data extraction, preprocessing, exploratory data analysis, feature engineering, and building machine learning models (GLMs, tree-based, neural nets) to generate actionable business insights.
Translate analytical insights into recommendations for managerial review and cross-functional teams, collaborating with internal partners and contributing to knowledge of credit card operations, banking, finance, and related regulations.
Research and test new data science tools, algorithms, and platforms to improve predictive modeling; manage model risk by applying governance controls and collaborating on validations and audits.
Bachelor’s Degree in statistics, mathematics, engineering, data science, economics, computer science, or other quantitative field.
5-8 years of experience in software domain with hands-on data science experience.
Proficiency in Python, Spark SQL, SQL, and experience with large data extraction and processing.
Work Environment: Normal office environment, hybrid; Work Experience Required: 5-8 years; Notice Period: Not explicitly mentioned.
Expertise in deploying and validating advanced machine learning models in a business environment, particularly in finance or credit card operations domain.
Experienced in translating complex model insights into actionable business strategies communicated across managerial and cross-functional stakeholders.
Comfortable working in regulated environments with model risk governance and able to collaborate effectively with risk and audit teams.