Risk Data Scientist - Credit

What you'll do

• Build end-to-end AI models (statistical, machine learning, deep learning, LLM) to support key risk processes across retail and SME lending, including fraud detection, risk assessment, underwriting, and product/limit optimization.
• Perform data mining and feature engineering using large-scale transactional, behavioral, and financial data, ensuring high-quality datasets for modeling.
• Manage the full model lifecycle: development, deployment, production support, and performance monitoring.
• Analyze model performance and extract data insights to guide decision-making and strategy.
• Explore advanced modeling techniques (eg, sequential models, graph learning, uplift modeling, causal inference) to solve complex business challenges and improve profitability and risk control.
• Collaborate closely with Strategy, Business, and Engineering teams to translate business problems into modeling solutions and drive implementation.

Requirements

• Bachelor’s degree or above in Computer Science, Mathematics, Statistics, Quantitative Finance, or related field; Master’s degree preferred.
• Hands-on internship or project experience in applied machine learning or data science; experience in credit risk or anti-fraud modeling is a plus.
• Proficient in Python with experience in ML/DL frameworks such as scikit-learn and PyTorch.
• Strong SQL skills; experience with big-data tools (eg, Hadoop, Spark) is highly preferred.
• Solid understanding of machine learning algorithms; expertise in at least one of the following areas is a plus: sequential modeling, graph learning, causal inference, multi-task learning, reinforcement learning, transfer learning.
• Self-driven, proactive, positive mindset, and strong communication and collaboration skills.