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Tier-1 brand, mid-level ML role, metro location, and generalist Data Scientist title drive high competition.
High due to required financial risk modeling experience and domain-specific fraud prevention expertise.
Mandatory five-year financial risk modeling experience and specific ML, cloud, and tooling requirements.
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Develop, train, deploy, and monitor machine learning models for fraud prevention and risk management in an AWS cloud environment.
Lead technical strategy and analytical direction for fraud prevention, including building scalable, reusable machine learning solutions and mentoring junior team members.
Collaborate cross-functionally to align AI-driven modeling solutions with business and firmwide objectives, leveraging modern tools like Databricks, PySpark, and advanced AI architectures such as Graph Networks and Agentic AI.
Master's degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field, or equivalent experience.
At least 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, building data pipelines using Databricks/PySpark, and AWS cloud environments.
Experienced in both classical and deep learning machine learning methods with a deep technical understanding beyond library usage.
Demonstrated ability to build and optimize AI agents and advanced architectures like Graph Networks and Agentic AI with focus on model impact in live financial transactions.
Capable of leading technical teams and driving scalable, firmwide machine learning solutions aligned with product and business goals in a financial institution environment.