CCB Risk Program Associate

What you'll do

• Design and develop machine learning models to drive fraud modeling across the customer lifecycle.
• Apply deep learning, transformer-based models, and LLMs to solve complex business challenges on big data platforms.
• Build vision and multimodal AI systems for image understanding and visual anomaly detection.
• Develop synthetic data and image generation workflows using Generative AI.
• Implement model evaluation, monitoring, and MLOps tooling to ensure scalability and auditability.
• Deploy production-ready models while ensuring compliance with governance and regulatory expectations.
• Perform data wrangling and model building on distributed cloud computation environments.
• Apply prompt engineering and fine-tuning techniques to large language models with appropriate safeguards.

What we're looking for

• Ph.D. or Master’s degree in a quantitative discipline such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
• 5+ years of experience creating predictive models and generative AI solutions using LLM prompt engineering.
• Hands-on experience with LLM APIs and Python libraries including Pandas, NumPy, and scikit-learn.
• In-depth knowledge of advanced machine learning algorithms, including logistic regression, XGBoost, Deep Neural Networks, clustering, and recommendation systems.
• Proficiency in Python, TensorFlow, PyTorch, Spark, or Scala, plus experience with Hadoop, AWS, Hive, and MLOps tooling.
• Experience in model interpretability and explainability for complex models, specifically extending these methods to deep learning architectures (preferred).
• Proven track record in designing, building, and deploying high-quality machine learning models in production environments (preferred).
• Demonstrated expertise in data wrangling and model building on a distributed Cloud computation environment with GPU experience (preferred).