At JPMorgan, we are not looking for job seekers. We seek change makers who want to make an impact.

As an AI Machine Learning Senior Associate within our dynamic team, you will be tasked with fostering long-term profitable growth by utilizing your robust business acumen. You will also be expected to work effectively within a team setting and present results to senior management in a clear and concise manner. As a part of the Consumer and Community Banking Risk Fraud Modeling team, you will be responsible for the development and implementation of top-tier fraud prevention and authentication models. This team provides a variety of models and analytical tools aimed at enhancing customer authentication experience and identifying potentially fraudulent transactions across various business lines

Job responsibilities-

• Build machine learning models that assess authentication confidence and predict fraudulent events risk.
• Participate and provide support throughout full cycle of the modeling projects, including data exploration, model development, quality control, and performance monitoring and model review.
• Maintain partnership with business stakeholders. Collaborate to identify opportunities, collect business needs into model development and communicate model results to business users.
• Conduct data science from big data and provide guidance to business on leveraging the modeling solutions.
• Collaborate with IT teams to streamline, improve and deploy production models in cloud platforms and support firmwide technology enhancements.
• Ensure models follow regulatory requirements and firm wide risk/control policies.
• Work with colleagues with various backgrounds to maintain a productive and inclusive community.

Required qualifications, capabilities and skills -

• Master or PhD degree in Statistics, Engineering, Computer Science, Mathematics, Physics, or other related quantitative fields of study.
• Skills with analytical thinking and problem solving that demonstrate candidates able to understand the business contexts, form hypothesis with potential approaches leading to solutions, search for data, and build up a model to ingest data to solutions.
• Experience on model development with statistical analysis and machine learning techniques such as Regressions, decision trees, Gradient Boosting, and Neural Networks. Depth knowledge.
• Experience on model development systems such as AWS Cloud, Sagemaker, Apache Spark, Snowflake.
• Skills with programming languages such as SQL, Python and PySpark
• Communication skills that explain ML/AI model details to audience with various backgrounds.

Preferred qualifications, capabilities and skills -

• Experience on sophisticated ML/AI methods like Deep Learning, NLP, LLM, Graph are further preferred.
• Experience with Graph database and Graph modeling are further preferred.