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Tier-1 bank, mid-level ML role, metro location, broad skillset requirements increase candidate competition.
Role requires financial-institution fraud modelling experience, reducing cross-industry transferability.
Explicit 5+ years, mandatory financial risk modeling experience, and specific ML/cloud tech requirements.
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Develop, train, deploy, and monitor machine learning models for fraud prevention and risk management in a financial institution environment.
Research and implement advanced architectures and AI agents, ensuring reliability and enhancing user experience.
Lead technical strategy, mentor junior members, and collaborate cross-functionally to align solutions with business goals and build scalable fraud prevention capabilities.
Master's degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field, or equivalent experience.
Minimum 5 years of experience in developing and managing predictive risk models in financial institutions.
Proficient in Python, SQL or PySpark, with experience in deep learning frameworks (PyTorch/TensorFlow) and classical machine learning tools (XGBoost, Scikit-learn).
Experience with large data sets and data pipelines using Databricks, PySpark, AWS cloud environment experience, and ability to build/test AI agents.
Experienced in both classical and deep learning machine learning methods with a deep understanding of underlying mathematics.
Strategic thinker able to build scalable, reusable solutions impacting live financial transactions with a product-first mindset.
Capable of leading technical direction and mentoring, with strong collaboration skills across product, engineering, and data science teams.