Job Description

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Chase Consumer & Community Banking (CCB) serves consumers and small businesses with a broad range of financial services, including personal banking, small business banking and lending, mortgages, credit cards, payments, auto finance and investment advice. CCB Risk partners with each CCB sub-line of business to identify, assess, prioritize and remediate risk. Types of risk in consumer businesses include fraud, reputation, operational, credit, market and regulatory.

The Risk Modeling Team is a center of excellence within CCB Risk. We focus on statistical and machine learning (ML) techniques (including deep learning) to develop models that are used in credit risk assessment and fraud prevention. We are looking for candidates with demonstrated expertise in ML to join our talented team. The candidate will be responsible for end-to-end statistical and ML model development.

Success in this role requires a strong foundation in statistics and ML, coupled with experience in big data and distributed computing. The successful candidate will think like an analytic leader, collaborate with other teams, and communicate results effectively to senior management.

Risk Modeling - Data Scientist (VP)

• Utilize statistical and machine learning techniques to lead model development for business banking models.
• Steer models throughout their life cycle, including development, implementation, use, monitoring, and retirement.
• Collaborate with partners in marketing, risk, technology, and model governance.

Qualifications

• Minimum 8+ years work experience with 3 years of people management experience.
• Master's degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
• Experience in designing, building, and deploying production quality models, utilizing statistical and/or ML techniques.
• Deep understanding of tuning and testing ML models (e.g., XGBoost, CNN, RNN)

About Us:

JPMorgan Chase & Co., 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. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.

About the Team:

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.