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.
Open to candidates across experience levels, including entry-level and experienced professionals.
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.
