Applied AI ML Associate at JPMC Candidate Experience page

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

12 Aug, 26

Salary

0.0

Posted On

14 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Statistics, Python, Data Analysis, Fraud Modelling, Credit Risk Modelling, XGBoost, LightGBM, Neural Networks, NumPy, Scikit-Learn, Pandas, Generative AI, Model Governance, Feature Engineering, Model Surveillance

Industry

Financial Services

Description

The ICB (International Consumer Banking) business within JPMorgan Chase has grown significantly since its launch in 2021, and we expect the business to expand further over the next few years. Join the expansion of the Chase digital bank across the UK and Europe, and help us continue to build our award-winning bank. The ICB Risk Modelling team is responsible for developing statistical and machine learning models to reduce fraud and credit risk within ICB. The team also engages with external vendors and supports the onboarding of vendor models, including working through Model Governance to obtain appropriate approvals for different uses. The team executes and prepares model surveillance while providing insights for various regulatory requirements. As an AI/ML Associate in the ICB Risk Modelling team, you will play a crucial role in analysing business problems and onboarding/developing and managing machine learning vendor/inhouse models used to mitigate fraud & credit risk within ICB. You will work closely with product owners, risk officers, data engineers, software engineers, and external vendors to ensure models perform effectively and meet the firm's standards. We are looking for someone with a strong foundation in statistics, machine learning, and data analysis, who can understand and support the strategic objective and data landscape of large, complex organisations. Job Responsibilities * Own end-to-end onboarding and lifecycle management of vendor models used for fraud and credit risk decisioning. * Evaluate and adopt state-of-the-art models and vendor capabilities for fraud and credit risk, providing effective challenge through independent assessment of model methodology, assumptions, inputs, limitations, and performance etc. * Develop in-house fraud and credit risk models end-to-end, applying statistical and machine learning techniques (e.g., regression, XGBoost/LightGBM, neural networks etc.) across feature engineering, model training, testing/benchmarking, and deployment readiness. * Design and execute ongoing performance monitoring for vendor and in-house models, leveraging multi-dimensional aggregation (e.g., time, geography, portfolio/segment, channel, customer and transaction/loan attributes) to track stability, drift, and outcomes. Conduct root-cause analysis for emerging trends and performance shifts, quantify business impact, and communicate clear findings and recommendations to senior management and partners. * Prepare and maintain governance, audit, and regulatory materials supporting vendor/inhouse model oversight, model surveillance, and required documentation standards. * Work with multiple partner teams—including Strategy, Technology, Product Management, Legal, Compliance, Business Management, and Model Governance — to ensure the models meet the firm's high governance standards and regulatory requirements, and support audit and other business functions around model management. * Expected to work on multiple projects with modeling teams in other locations, to ensure high quality model development standards, reviews and re-reviews, model monitoring, enhanced model usage support etc. in compliance with firm’s Estimation policies and procedures. Ensure right development and usage of models owned. Required Qualifications, Capabilities, and Skills * 4+ years’ statistical Machine learning model development/validation experience in a deeply quantitative role in the financial services industry or Fintech’s dealing with advanced analytical or machine learning methods. * A Master’s or Ph.D. Degree in a technical or quantitative field such as Statistics, Economics, Finance, Mathematics, Computer Science, Engineering from Top-Tier university * Solid understanding of fraud and/or credit risk modelling in financial organizations, including the unique challenges and regulatory considerations involved. * Proficient in Python, with hands-on experience in data analysis and writing production-quality code. * Extensive experience with machine learning and data analysis toolkits (e.g., NumPy, Scikit-Learn, Pandas). * Ability to effectively leverage Generative AI tools to enhance productivity, analysis, and problem-solving in day-to-day work. * Strong written and spoken communication skills to effectively convey technical concepts and results to both technical and business audiences. Team player. Preferred Qualifications, Capabilities, and Skills * Experience with ML model development and understanding model governance processes. * Experience with model risk management frameworks. 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 [https://careers.jpmorgan.com/us/en/how-we-hire/faqs] for more information about requesting an accommodation. 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.

Responsibilities

The role involves the end-to-end development and lifecycle management of in-house and vendor machine learning models to mitigate fraud and credit risk. It also requires designing performance monitoring systems and ensuring compliance with firm governance and regulatory standards.