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Tier-1 brand, metro location, and mid-level experience drive high applicant competition despite specialized skills.
Causal ML skills are transferable, but banking product and regulatory experience increase domain sensitivity.
Mandatory 2+ years, deep causal ML expertise, production deployment experience, and regulatory governance raise strictness.
Own end-to-end delivery of AI/ML analytics models including development, implementation, monitoring, and governance.
Lead causal inference modeling to measure business decision impacts and provide actionable insights.
Collaborate with technical and business teams to deploy models in production and ensure compliance with regulatory and internal standards.
At least 2 years of quantitative analytics experience demonstrated via work experience, training, or education.
Bachelor's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or related quantitative discipline.
Proficiency in Python, R, SAS, C++, and SQL for statistical and mathematical modeling.
Experience with financial products and risk management analytics.
Hands-on expertise in developing and validating advanced causal inference models (e.g., T-Learners, Causal Forests) and AI/ML models in applied business environments.
Strong foundation in statistics, machine learning, experimental design, and large-scale data analysis with skills in optimization and simulation techniques.
Ability to translate complex analytical findings into clear, actionable business recommendations for senior leadership and collaborate cross-functionally.