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Tier-1 employer, metro location, mid-level role, and generalist analytics title increase candidate competition.
Requires credit-card and consumer banking analytics expertise, making industry crossover difficult.
Explicit 5–7 years, banking analytics experience, and mandatory PySpark/Python/SAS/SQL increase strictness.
Collect and analyze operational data from multiple stakeholders to assess past business performance and identify patterns and trends.
Deliver insights and recommendations to improve business planning, process improvements, campaign optimization, and strategic opportunities in Credit Card Marketing.
Collaborate with business partners and senior leaders to translate data into consumer behavior insights that drive targeting, segmentation, and decision strategies, while incorporating risk assessment and compliance awareness.
5-7 years of experience in Consumer Banking analytics with focus on Financial Analytics for consumer banking.
Proficient in PySpark, Python, SAS, and SQL; experience with Agentic AI development is a plus but not required.
Master's degree or equivalent experience is mandatory.
Preferred domain experience 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 strong grasp of financial performance metrics and campaign management.
Capable of independently handling complex data analysis projects with measurable business impact and providing actionable insights.
Effective communicator who can present complex behavioral data insights clearly to business stakeholders and senior leadership.