Applied AI/ML Senior Associate

As part of the Commercial & Investment Bank, J.P. Morgan Payments enables organizations of all sizes to execute transactions efficiently and securely, transforming the movement of information, money and assets. We tackle complex challenges at every stage of the payment lifecycle and our industry-leading solutions facilitate seamless transactions across borders, industries and platforms.  Operating in over 160 countries and handling more than 120 currencies, we are the largest processor of USD payments, with a daily transaction volume of $10 trillion.

 

As a Senior Associate Applied AI/ML Scientist within our payment solutions team, you will be instrumental in utilizing artificial intelligence and machine learning technologies to augment our services and stimulate business expansion. Your role will involve researching, experimenting, developing, and implementing high-quality machine learning models, services, and platforms to streamline payment processes, bolster fraud detection, and enrich customer experience. You will also be tasked with designing and executing highly scalable and dependable data processing pipelines, conducting analysis, and deriving insights to boost and optimize business outcomes. Collaborating with cross-functional teams to pinpoint opportunities for AI/ML applications within the payment’s ecosystem will also be a part of your responsibilities. 

 

Job Responsibilities: 

• Actively collaborate with Product, Technology, and other cross-functional teams to gain a deep understanding of complex business problems and formulate data-driven solutions to address these challenges in key areas of the payments’ domain. 
•  Design, develop, and deploy agentic systems using Large Language Models (LLM), machine learning and other AI solutions that meet success metrics aligned with business goals, while considering constraints such as model complexity, scalability, and latency. 
• Partner with Risk and Compliance teams to ensure comprehensive model documentation, track performance metrics, and maintain adherence to regulatory compliance standards. 
•  Translate model outcomes into business impact metrics and communicate complex concepts to senior management and stakeholders. 

 

Required qualifications, capabilities, and skills: 

• Master’s degree in a quantitative discipline (e.g., Computer Science, Data Science, Mathematics/Statistics, or Operations Research) with a minimum of 3 years of industry experience. Experience with Shell Scripting, Jupyter notebook/Lab, SQL, PySpark, and AWS Cloud Services is required. 
• Proficient in Python with hands-on experience in Machine learning and Deep learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., NumPy, Scikit-Learn, Pandas). Experience with Jupyter Notebook/Lab is essential. 
• Extensive knowledge in the design and development of agentic systems and the tooling ecosystem such as LangChain, LangGraph, Model Context Protocol (MCP), DSPy, etc.   
• Solid Understanding of algorithms in machine learning, AI, and neural network, including Large Language Models (LLM) and Generative AI as well as familiarity with state-of-the-art practices and advancements in these domains. 
• Ability to set the analytical direction for projects, transforming vague business questions into structured analytical plans. You possess strong cognitive and communication skills, characterized by clear and articulate expression. You excel at identifying core issues, bringing order to chaos, synthesizing insights, and driving decisive outcomes. 
•  Extensive experience in Natural Language Processing (NLP) or Large Language Models (LLM),or Computer Vision and other machine learning techniques, including classification, regression algorithms. 

 

Preferred Qualifications, capabilities and skills 

• Experience in the financial services industry, particularly within investment banking operations. 
• Cloud computing:  Amazon Web Service, Azure, Docker, Kubernetes, DataBricks, Snowflakes. 
• Familiarity with inference-time algorithms such as Chain of Thought and sampling, etc.