Reshaping the future of Consumer and Community Banking (CCB) by building advanced solutions in areas such as credit decision, fraud detection, and loan loss reserve.
Requirements
Knowledge of machine learning / data science theory, techniques and tools
Knowledge and proficiency in Python
Experience with modern machine learning tools such as PyTorch and XGBoost
Experience with data tools such as Hadoop, PySpark and SQL
Experience with Java, C++, C-Sharp, Rust an additional bonus
Knowledge in Reinforcement, Meta, Graph or Transfer learning
Knowledge in newer deep learning architecture such as Transformer or Mamba
Responsibilities
Explore cutting-edge research in partnership with leading academic institutions, applying the latest Machine Learning techniques or Data Science Theories to CCB’s unique data assets
Collaborate with stakeholders to drive model-driven transformation across business
Focus on Deep Learning, Reinforcement Learning, Natural Language Processing, Speech/Voice Analytics, Time Series, Computer Vision, Cryptography, and Interpretability, and Ethics and Fairness of AI
Follow the protocol and procedures when dealing with sensitive data, and firm’s policy and guideline when interacting with control and governance bodies
Work with other team members in a collaborative and inclusive manner
Other
Enrolled in a Ph.D. or 2-year Master’s degree program in math, science, engineering, computer science or other quantitative fields with an expected graduation date of December 2026 through August 2027
Excellent analytical, quantitative and problem-solving skills and demonstrated research ability
Strong communication skills and the ability to present findings to a non-technical audience
