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Tier-1 brand, mid-level experience band, and metro location create high applicant competition.
Role requires domain-specific quantitative finance, regulatory modeling, and causal-inference expertise, so background fit is highly sensitive.
Mandatory 4+ years, specific causal-ML skills, and banking governance requirements make shortlisting highly strict.
Deliver high-impact analytics and AI/ML solutions across the end-to-end model lifecycle including development, implementation, monitoring, and governance.
Lead development and application of causal inference methodologies to measure business impact and support strategic decisions.
Collaborate with cross-functional teams and regulators to ensure model performance, explainability, compliance, and translate causal insights into actionable recommendations.
4+ years of quantitative analytics experience.
Bachelor's degree or higher in a quantitative discipline (mathematics, statistics, engineering, physics, economics, computer science).
Strong programming skills in Python, PySpark, and SQL with experience handling large datasets.
Work Experience Required: 4+ years in quantitative analytics or equivalent experience.
Hands-on expertise in AI/ML model development and causal inference techniques (e.g., T-Learners, S-Learners, causal forests, propensity score methods).
Experience implementing optimization models and strong foundation in statistics, machine learning, and experimental design.
Ability to communicate complex causal findings effectively to technical and non-technical stakeholders and collaborate cross-functionally to operationalize models.