Consumer & Community Banking Risk Modeling Internship Program.

Our AI and Data Science teams combine data science, research, and industry expertise to develop innovative solutions for complex financial challenges. By leveraging technologies like AI, blockchain, big data, machine learning, and robotics, we enhance customer and client experiences and promote impactful, real-world applications in a collaborative, entrepreneurial environment.

As a Summer Intern within the Consumer and Community Banking (CCB) Risk Modeling teams, you will build advanced solutions in areas such as credit decision, fraud detection, and loan loss reserve. Throughout the internship program, your professional growth and development will be nurtured through project work aligned with your academic and professional interests, mentorship, and engaging speaker series with senior leaders. Successful completion of the program may lead to full-time employment offers.

Job 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

Required qualifications, capabilities and skills

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 2027 through July 2028.
Knowledge of machine learning / data science theory, techniques and tools
Knowledge and proficiency in Python
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

Preferred qualifications, capabilities and skills

Experience with modern machine learning tools such as PyTorch and XGBoost
Experience with data tools such as PySpark and SQL
Experience with Java, C++, C#, Rust an additional bonus
Knowledge in Reinforcement, Meta, Graph or Transfer learning
Knowledge in newer deep learning architecture such as Transformer or Mamba
Knowledge in advanced mathematics arising in financial modeling such as Probability Theory, Monte-Carlo Simulation, Markov Chain and Econometric Theories

We will be filling our classes on a rolling basis. We strongly encourage you to submit your application as early as possible before job postings close.