CCB Risk Modeling Data-Scientist Sr Associate Fraud Prevention

JPMorgan Chase & Co.; Bengaluru; Vor Ort; INR 900.000 - 1.500.000; Vollzeit

Zusammenfassung

JPMorgan Chase & Co. in Bengaluru seeks an experienced machine learning professional to shape the team’s technical direction for fraud prevention. You will build long-term, firmwide capabilities using cutting-edge cloud tools in an AWS environment.

You will develop, train, and deploy models, explore Graph Networks and Agentic AI, and collaborate with product and data science teams to deliver scalable, reliable AI solutions for risk management and decision support.

Qualifikationen

• Master’s degree in Computer Science, Mathematics, Statistics, Economics, or related field, or equivalent work experience.
• Min. 5 years of predictive risk modeling experience in financial institutions.
• Strong understanding of ML theory and algorithms; experience with both classical and deep learning methods.
• Proficiency in Python, SQL or PySpark; experience with PyTorch or TensorFlow and XGBoost/Scikit-learn.
• Knowledge of graph analytics (GSQL is a plus).
• Experience with large datasets and data pipelines (Databricks, PySpark).
• Experience in AWS cloud environments and building AI agents; mentoring juniors.

Aufgaben

• Develop, train, and deploy ML models for fraud prevention and risk management.
• Research and implement architectures including Graph Networks, Agentic AI, and LLMs.
• Build and test AI agents; iterate designs for reliability and UX.
• Create data pipelines and dashboards with Databricks and PySpark.
• Monitor and optimize model performance in production.
• Lead technical strategy and guide analytics direction; foster innovation.
• Mentor junior team members and share best practices.
• Collaborate with product, engineering, and data science to align modeling with business goals.
• Contribute to scalable, reusable ML solutions and firm-wide fraud capabilities.

Kenntnisse

Python

SQL

PySpark

PyTorch

TensorFlow

XGBoost

Scikit-learn

Databricks

AWS

Graph analytics

GSQL

Model deployment

Ausbildung

Master’s degree in Computer Science/Math/Statistics/Economics

Tools

Databricks

PySpark

AWS

PyTorch

TensorFlow

XGBoost

Scikit-learn

Jobbeschreibung

Job Description:

In this role, you will serve as a hands-on individual contributor. You will help shape the technical direction of the team and build long-term, firmwide capabilities to identify and prevent fraud, leveraging cutting-edge techniques and modern cloud-based tools in an AWS environment.

Job Responsibilities:

• Develop, train, and deploy machine learning models for fraud prevention and risk management.
• Research and implement novel architectures, including Graph Networks, Agentic AI, and Large Language Models.
• Build and test AI agents, iterating designs to enhance functionality and user experience. Conduct rigorous testing to ensure reliability and effectiveness of AI solutions.
• Use tools like Databricks and PySpark to create data pipelines and dashboards that support AI-driven insights and decision-making.
• Monitor and optimize model performance in real-world environments, adapting to evolving fraud patterns.
• Lead technical strategy and guide analytical direction within the team, fostering a culture of innovation and continuous improvement.
• Mentor and support junior team members, sharing best practices and technical expertise.
• Collaborate with cross-functional teams—including product, engineering, and data science—to align modeling solutions with business objectives and firmwide priorities.
• Contribute to the development of scalable, reusable machine learning solutions and best practices that strengthen the firm’s overall fraud prevention capabilities.

Required Qualifications, Capabilities, and Skills:

• Master’s degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field, or equivalent work experience.
• Minimal 5-year of experience in developing and managing predictive riskmodels in financial institutions.
• Deep understanding of machine learning theory and algorithms, with hands-on experience in both classical and deep learning methods.
• Proficient in Python, SQL or PySpark with experience in deep learning frameworks such as PyTorch or TensorFlow, and classical machine learning tools like XGBoost or Scikit-learn.
• Knowledge of graph analytics including GSQL will be an added bonus.
• Experience working with large datasets and building data pipelines using Databricks, PySpark, or similar technologies.
• Experience working in AWS cloud environments.
• Ability to build and test AI agents, iterate designs, and conduct rigorous testing for reliability and effectiveness.
• Experience mentoring or coaching junior team members.

Preferred/Additional Qualifications:

• Experience or strong interest in Graph Analytics and Agentic AI.
• Knowledge of GSQL.
• Deep technical understanding of the mathematics behind algorithms, not just library usage.
• Product-first mindset, with a focus on the role models play in the user experience and overall product responsibility.
• Versatility in handling both tabular and non-tabular data using classical machine learning (e.g., trees/forests) and modern deep learning techniques.
• Driven by impact and energized by the responsibility of having your models make decisions on live financial transactions.
• Demonstrated ability to build scalable, reusable solutions that contribute to firmwide capabilities and long-term strategic goals.

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.

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.