Applied AI/ML Senior Associate - Payments

JPMorgan Chase & Co.; New York; Hybrid; USD 170,000 - 210,000; Full time

Job summary

JPMorgan Chase & Co. in New York seeks a Sr.

Associate Applied AI/ML Scientist within our Payment Solutions team to advance AI/ML across payment workflows, fraud mitigation, and customer experience. You will research, develop, and deploy scalable models and data pipelines, collaborating with product, technology, and risk teams to turn complex business questions into measurable, data-driven outcomes that drive growth and efficiency.

Qualifications

• Master’s degree in a quantitative discipline with at least 3 years of industry experience.
• Experience with Shell scripting, Jupyter notebook/Lab, SQL, PySpark, 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 and SageMaker pipelines for production ML.
• Proficient in Python with ML and DL frameworks (TensorFlow, PyTorch) and libraries (NumPy, Scikit-Learn, Pandas).
• 1+ years of NLP/LLM experience, and 3+ years in other ML techniques including classification and regression.
• Strong expertise in neural networks and Transformers, including fine-tuning strategies.
• Experience building data-driven systems using SQL and distributed processing (Spark/PySpark).
• Solid understanding of ML/AI, LLMs, Generative AI and modern best practices.

Responsibilities

• Actively collaborate with Product, Technology, and other cross-functional teams to formulate data-driven solutions in the payments domain.
• Design, develop, and deploy ML/AI solutions that meet business goals while considering model complexity, scalability, and latency.
• Partner with Risk and Compliance to ensure model documentation, performance tracking, and regulatory alignment.
• Translate model outcomes into business impact metrics and communicate insights to senior management.

Skills

Python

Machine Learning

Deep Learning

NLP

AWS

SageMaker

SQL

PySpark

Education

Master’s degree in a quantitative field

Tools

TensorFlow

PyTorch

Jupyter

Spark

Job description

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 Sr. 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 3 years of industry experience.
• Experience with Shell Scripting, Jupyter notebook/Lab, SQL, PySpark, 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 SageMaker training, pipelines, and model registry for production ML.
• 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.
• 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.
• Strong hands-on expertise inneural networks and Transformers, including practical experience with Fine-tuningstrategies (e.g., full fine-tuning and parameter-efficient methods)
• Experience building data-driven systems usingSQLand distributed processing (e.g.,Spark/PySparkor equivalent).
• 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.

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