CCB Risk Modeling Senior 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 3 years 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 Qualifications, capabilities and skills:


* 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.


* Versatili...