Job Description
The CCB Risk Modeling team i s seeking talented professionals with expertise in machine learning, explainable AI (XAI), and responsible AI practices, with a focus on credit decision and fraud modeling applications. Our work centers on explainability, fairness, and algorithmic bias — understanding how modern AI systems reason and make decisions across ML systems, next-generation LLMs, and agentic workflows. The ideal candidate will drive these initiatives across model development, tooling, and cross-functional collaboration, ensuring AI/ML solutions meet ethical standards and regulatory expectations.
Key Responsibilities Model Development:
Design and develop machine learning models to drive impactful decisions across credit decisions and 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. Explainability & Fairness:
Develop and maintain tools and frameworks that enhance AI/ML model explainability and fairness, ensuring transparency and ethical use of models. 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. 2 years of experience with data analysis in Python. Proven track record in designing, building, and deploying high-quality machine learning models in production environments, demonstrating a strong ability to translate theoretical concepts into practical applications. 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 auditability. Preferred Qualifications Strong expertise, interest, and track record of performing cutting-edge research on Explainable AI (XAI) and LLM. Demonstrated expertise in data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). GPU experience is desired. Strong ownership and execution; proven experience in implementing models in production.
