CCB Risk Modeling Data-Scientist Sr Associate Fraud Prevention

JPMorgan Chase · ✓ Verified company
📍 Bengaluru, Karnataka, India · On-site · Full-time

About the role

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 risk models 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.