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Tier-1 brand, metro locations, mid-level ML role, and broad toolset increase applicant competition.
Requires bank-specific fraud/risk modeling experience, making cross-industry transferability low.
Explicit 5+ years in financial risk modeling plus advanced ML and tool requirements make filters strict.
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Develop, train, deploy, and monitor machine learning models for fraud prevention and risk management in an AWS environment.
Lead technical strategy and guide analytical direction for the team, building scalable, reusable machine learning solutions that enhance firmwide fraud prevention capabilities.
Build and test AI agents using advanced architectures (Graph Networks, Agentic AI, Large Language Models) and collaborate with cross-functional teams aligning solutions with business priorities.
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; experience with deep learning frameworks (PyTorch, TensorFlow) and classical ML tools (XGBoost, Scikit-learn).
Experience working with large datasets, Databricks, PySpark, AWS cloud environments; experience mentoring junior team members.
Experienced in advanced machine learning techniques including both classical and deep learning methods, with deep mathematical understanding of algorithms.
Skilled in building and iterating AI agents with focus on reliability, effectiveness, and product impact on live financial transactions.
Capable of leading technical direction in a collaborative, cross-functional environment, driving long-term strategic goals for firmwide fraud prevention enhancements.