CCB Risk Program Associate

The CCB Risk Modeling team is seeking talented professionals with expertise in computer vision, Generative AI, and image generation, with a focus on fraud modeling applications.

Our work centers on building vision and multimodal AI systems-including image understanding, visual anomaly detection, synthetic data/image generation, and GenAI-enabled workflows-across modern ML platforms and emerging agentic patterns.

The ideal candidate will drive these initiatives across model development, evaluation and monitoring tooling, and cross-functional collaboration, ensuring AI/ML solutions are robust, scalable, and aligned with governance, risk, and regulatory expectations.

Key Responsibilities

Model Development: Design and develop machine learning models to drive impactful fraud modeling, covering the entire customer lifecycle, including acquisition, account management, transaction authorization, and collections.

Advanced Machine Learning Techniques: Apply state-of-the-art machine learning methodologies - including deep learning architecture, transformer-based models, and LLMs - on big data platforms to tackle complex business challenges.

Strategic Collaboration: Work closely with senior management to develop and implement ambitious, innovative modeling solutions, ensuring their successful deployment into production environments.

Cross-Functional Partnership: Collaborate with diverse teams, including risk, technology, model governance, and research, throughout the entire modeling lifecycle-from development and review to deployment and operational use.
Basic Qualifications

Ph.D.

or Master's degree from a reputable institution in a quantitative discipline such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.

5+ years' experience in creating predictive models, and generative AI solutions using LLM prompt engineering.

Hands-on experience with LLM APIs, Python libraries like Pandas, NumPy, scikit-learn, and others for data manipulation, modeling and analysis.

In-depth knowledge of advanced machine learning algorithms, including logistic regression, XGBoost, Deep Neural Networks (CNN and RNN), clustering, and recommendation systems, with expertise in model design, hyperparameter tuning, and responsible deployment practices.

Demonstrated experience in model interpretability and explainability for complex models such as XGBoost and GBM; experience extending these methods to deep learning architectures (CNNs, RNNs, transformers) is a strong plus.

Familiarity with large language models (LLMs) and their applications, including experience in fine-tuning, prompt engineering, and responsible deployment with appropriate safeguards, monitoring, and auditability.

Proficiency in Python, TensorFlow, PyTorch, Spark, or Scala, coupled with experience in big data technologies such as Hadoop, AWS, and Hive, and familiarity with MLOps tooling that supports model monitoring, drift detection, and end-to-end auditabili...