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 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 payment solutions and stimulate business expansion. Your role will involve researching, experimenting, developing, and transitioning high-quality machine learning models, services, and platforms into production 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. Working in collaboration with cross-functional teams, you will identify opportunities for AI/ML applications within the payments ecosystem.

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 machine learning and 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 2 years of industry experience. Experience with Shell Scripting, Jupyter notebook/Lab, SQL, Py Spark, and AWS Cloud Services is required.

• 2+ years of hands‑on experience with large‑scale data processing on AWS EMR, building robust batched feature stores (offline/online pipelines, schema governance, backfills, reproducibility), and orchestrating Sage Maker training, pipelines, and model registry for production ML.

• Proficient in Python with hands-on experience in Machine learning and Deep learning frameworks (e.g., Tensor Flow, Py Torch) and libraries (e.g., Num Py, Scikit-Learn, Pandas). Experience with Jupyter Notebook/Lab is essential.

• 1+ years of extensive experience in Natural Language Processing (NLP) or Large Language Models (LLM), AgenticAI, and 3+ years of extensive experience in other machine learning techniques, including classification, regression algorithms.

• 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.

• Proficient in both basic and advanced exploratory data analysis (EDA), with an understanding of the limitations and implications of different methodologies.

• 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.

Preferred Qualifications, capabilities and skills

• Experience in the financial services industry, particularly within investment banking operations.

• Cloud computing: Amazon Web Service, Azure, Docker, Kubernetes, Data Bricks, Snowflakes.

• Trust & Safety (T&S) fraud experience in payments, designing and deploying ML models for account takeover, transaction fraud, promotion abuse