CCB Risk Program Associate

JPMorgan Chase & Co. • Plano, TX

2d

About The Position

As a Risk program Senior Associate within the Chase consumer Bank, you'll be the analytical expert for identifying and retooling suitable machine learning algorithms that can enhance the fraud risk ranking of particular transactions and/or applications for new products. This includes a balance of feature engineering, feature selection, and developing and training machine learning algorithms using cutting edge technology to extract predictive models/patterns from data gathered for billions of transactions. Your expertise and insights will help us effectively utilize big data platforms, data assets, and analytical capabilities to control fraud loss and improve customer experience.

Chase is a leading financial services firm, helping nearly half of America’s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.

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.

We offer a broad array of credit cards to meet the needs of individuals and small businesses, including Chase-branded and co-branded cards in partnership with well-known companies and organizations. Merchant Services is a leading provider of payment, fraud and data security for companies, capable of authorizing transactions across global currencies.

Requirements

• Master's degree in Mathematics, Statistics, Economics, Computer Science, Operations Research, Physics, and other related quantitative fields.
• 2 years of experience with data analysis in Python.
• Some Experience in designing models for a commercial purpose using some (at least 3) of the following machine learning and optimization techniques: CNN, RNN, SVM, Reinforcement Learning, Random Forest/GBM.
• A strong interest in how models work, the reasons why particular models work or not work on particular problems, and the practical aspects of how new models are designed.

Nice To Haves

• PhD in a quantitative field with publications in top journals, preferably in machine learning.
• Experience with model design in a big data environment making use of distributed/parallel processing via Hadoop, particularly Spark and Hive.
• Experience designing models with Keras/TensorFlow on GPU-accelerated hardware.
• Experience with graph technology, including designing and implementing graph-based machine learning models for fraud detection or risk assessment. Familiarity with graph databases (such as TigerGraph or Neo4j …), graph algorithms (e.g., node classification, link prediction, community detection), and graph feature engineering is highly desirable. Ability to leverage graph analytics to uncover complex relationships and patterns within large-scale transaction data is a strong plus.
• Hands-on experience with transformer models and related architectures (such as BERT, GPT, or Graph Transformers) for natural language processing, anomaly detection, or transaction analysis. Proficiency in fine-tuning and deploying transformer-based models using frameworks like PyTorch or TensorFlow is preferred. Demonstrated ability to apply transformer models to extract meaningful insights from unstructured or semi-structured data sources will be highly valued.

Responsibilities

• Identify and retool machine learning (ML) algorithms to analyze datasets for fraud detection in the Chase Consumer Bank.
• Perform machine learning tasks such as feature engineering, feature selection, and developing and training machine learning algorithms using cutting-edge technology to extract predictive models/patterns from billions of transactions’ amounts of data.
• Collaborate with business teams to identify opportunities, collect business needs, and provide guidance on leveraging the machine learning solutions.
• Interact with a broader audience in the firm to share knowledge, disseminate findings, and provide domain expertise

Benefits

• We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location.
• Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.
• We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more.
• Additional details about total compensation and benefits will be provided during the hiring process.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level