2027 Consumer & Community Banking - Risk Modeling Associate Program - Summer Internship

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 Hadoop, 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.