CCB Risk Modeling Data-Scientist Sr Associate Fraud Prevention

In this role, you will serve as a hands-on individual contributor. You will help shape the technical direction of the team and build long-term, firmwide capabilities to identify and prevent fraud, leveraging cutting-edge techniques and modern cloud-based tools in an AWS environment.

Job Responsibilities:

• Develop, train, and deploy machine learning models for fraud prevention and risk management.
• Research and implement novel architectures, including Graph Networks, Agentic AI, and Large Language Models.
• Build and test AI agents, iterating designs to enhance functionality and user experience. Conduct rigorous testing to ensure reliability and effectiveness of AI solutions.
• Use tools like Databricks and PySpark to create data pipelines and dashboards that support AI-driven insights and decision-making.
• Monitor and optimize model performance in real-world environments, adapting to evolving fraud patterns.
• Lead technical strategy and guide analytical direction within the team, fostering a culture of innovation and continuous improvement.
• Mentor and support junior team members, sharing best practices and technical expertise.
• Collaborate with cross-functional teams-including product, engineering, and data science-to align modeling solutions with business objectives and firmwide priorities.
• Contribute to the development of scalable, reusable machine learning solutions and best practices that strengthen the firm’s overall fraud prevention capabilities.

Required Qualifications, Capabilities, and Skills:

• Master’s degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field, or equivalent work experience.
• Minimal 5-year of experience in developing and managing predictive risk models in financial institutions.
• Deep understanding of machine learning theory and algorithms, with hands-on experience in both classical and deep learning methods.
• Proficient in Python, SQL or PySpark with experience in deep learning frameworks such as PyTorch or TensorFlow, and classical machine learning tools like XGBoost or Scikit-learn.
• Knowledge of graph analytics including GSQL will be an added bonus.
• Experience working with large datasets and building data pipelines using Databricks, PySpark, or similar technologies.
• Experience working in AWS cloud environments.
• Ability to build and test AI agents, iterate designs, and conduct rigorous testing for reliability and effectiveness.
• Experience mentoring or coaching junior team members.

Preferred/Additional Qualifications:

• Experience or strong interest in Graph Analytics and Agentic AI.
• Knowledge of GSQL.
• Deep technical understanding of the mathematics behind algorithms, not just library usage.
• Product-first mindset, with a focus on the role models play in the user experience and overall product responsibility.
• Versatility in handling both tabular and non-tabular data using classical machine learning (e.g., trees/forests) and modern deep learning techniques.
• Driven by impact and energized by the responsibility of having your models make decisions on live financial transactions.
• Demonstrated ability to build scalable, reusable solutions that contribute to firmwide capabilities and long-term strategic goals.