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Tier-1 bank, metro location, mid-level analytics role with broad technical requirements increases applicant density.
Role requires audit-specific domain knowledge and banking controls, making skills less transferable across industries.
Explicit 6–8 years, mandatory SQL/Python/Spark skills and banking audit knowledge enforce strict filters.
Lead design and execution of advanced audit analytics and automation solutions across the audit lifecycle in a global banking environment.
Develop and deploy AI-driven analytics pipelines leveraging large language models and autonomous frameworks to enhance audit coverage and efficiency.
Collaborate with audit, technology, and business teams to identify data needs, deliver actionable insights, and support continuous innovation in audit processes.
6-8 years of experience as a business or audit analyst delivering analytical and automated solutions.
Master's degree in Computer Science, IT, Mathematics, Statistics, Finance, or related quantitative field.
Proficiency in SQL, Python, Hadoop Ecosystem (Hive, PySpark), and data visualization tools (Tableau, MicroStrategy, Cognos).
Experience applying AI tools including LLMs and AI orchestration frameworks in audit or risk analytics contexts.
Experienced working in large, complex global organizations with exposure to multiple banking domains including Consumer, Institutional, Markets, AML, and Regulatory Reporting.
Capable of managing end-to-end analytics projects and driving innovation using cutting-edge AI and automation technologies within audit environments.
Strong collaborator able to communicate complex technical information clearly to diverse stakeholders and partner effectively across audit and technology teams.