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Tier-1 brand, metro location, mid-level generalist analytics role with common Python/SQL skills.
Credit card marketing, Cards P&L and campaign optimization require banking domain expertise, limiting transferability.
Explicit 5-7 years, banking Cards domain expertise and mandatory PySpark/Python/SAS/SQL make filters strict.
Gather and analyze operational data from cross-functional teams to assess past business performance and identify data patterns and trends.
Provide insights and recommendations to enhance business planning, process improvement, marketing strategies, and operational policies related to credit card marketing.
Translate data into consumer behavioral insights to drive targeting and segmentation strategies, and communicate findings to business partners and senior leaders.
5-7 years of experience in Consumer Banking analytics or Financial Analytics for consumer banking.
Proficiency in PySpark, Python, SAS, and SQL is mandatory.
Master's degree or equivalent experience is required.
Preferred background in Credit Card Marketing analytics, including understanding of Cards P&L, Campaign Optimization, P&L simulation, Financial forecasting, and Marketing strategy.
Experienced in credit card marketing analytics with a strategic understanding of campaign optimization and financial forecasting.
Skilled in translating complex data into actionable business strategies, particularly in marketing targeting and segmentation.
Capable of working closely with internal and external partners to build and improve decision strategies while managing risk and compliance effectively.