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Tier-1 brand, metro location, and broad analytics skillset increase applicant density, but senior level tempers it.
Strong banking operations and regulatory knowledge required, though SQL/Python and BI skills remain transferable.
Mandatory 8+ years, specific BI, Python/PySpark, banking operations and AI experience increase filter strictness.
Drive development and implementation of analytical solutions for Banking Operations & Analytics within COO, focusing on spend and utilization insights with contextualized metrics.
Create, maintain, and automate reports and dashboards using tools like SQL, Python, Tableau to enable real-time business performance visibility and process optimization.
Serve as liaison between business and technology for data digitization, validation, and technical guidance including knowledge graph development and data governance.
8+ years experience in Reporting & Automation or Data/Information Management Analyst roles.
Proficiency in SQL, Python, SAS, PySpark, Tableau or similar business intelligence and automation tools.
Bachelor's degree in STEM required; Master's degree preferred.
Work Experience Required: 8+ years explicitly mentioned.
Experienced in banking operations analytics such as expense analytics, cash flow management, fraud analytics, or ROI analysis.
Demonstrates strong skills in data automation, scripting (Python), and building scalable analytics frameworks.
Experienced in deploying AI/Gen AI solutions integrated with financial/business analysis and operational risk analysis to impact key business drivers.