About the role
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.
ABOUT US
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.