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Tier-1 brand, metro locations, and a mid-level popular ML role raise applicant competition significantly.
Requires financial risk and fraud modeling expertise, reducing cross-industry portability of experience.
Explicit 5+ years in financial risk modeling, master's degree, and deep ML/cloud requirements make filters strict.
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Develop, train, and deploy machine learning models focused on fraud prevention and risk management in a financial institution.
Lead technical strategy and guide analytical direction for the team, fostering innovation and continuous improvement in fraud detection capabilities.
Build and maintain data pipelines, dashboards, and AI agents using tools such as Databricks, PySpark, and AWS cloud, ensuring model reliability and adapting to evolving fraud patterns.
Master's degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field, or equivalent work experience.
Minimum 5 years of experience developing and managing predictive risk models in financial institutions.
Proficiency in Python, SQL or PySpark, and experience with deep learning frameworks (PyTorch or TensorFlow) and classical ML tools (XGBoost, Scikit-learn).
Experience with large datasets, building data pipelines using Databricks or similar technologies, and working within AWS cloud environments.
Experienced in both classical and deep learning methods with a deep theoretical understanding of machine learning algorithms and mathematics behind them.
Capable of leading technical teams, mentoring juniors, and translating ML solutions into products with user experience focus and operational impact on live financial transactions.
Familiarity or interest in advanced topics like Graph Analytics, Agentic AI, GSQL, and building scalable, reusable ML solutions for firmwide fraud prevention capabilities.