Applied AI/ML Lead

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 Vice President 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 payments 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 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 6 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.

• 6+ years of extensive experience working in ML domains like Fraud prevention, Trust and Safety, Ads, Recommender systems with hands on experience working with both numerical and textual or image data.

• Solid Understanding and past experience applying ML models including decision trees, random forests, neural networks, graph models and Large Language Models (LLMs).

• 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.

• Bring an AI-ML first thinking to our Fraud and Risk solutions and thus achieving Operational Excellence across the Organization.

Preferred Qualifications, capabilities and skills

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

• Past experience working in Fraud prevention and Trust & Safety is a plus.

• Past experience working on Commercial Fraud/Risk solutions is a plus.

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