About the Role
As a Sr. Associate Applied AI/ML Scientist within the Payment Solutions team at J.P. Morgan Payments, you will be crucial in leveraging artificial intelligence and machine learning technologies to enhance payment solutions and drive business growth. This role involves researching, experimenting, developing, and deploying high-quality machine learning models, services, and platforms into production. Your efforts will streamline payment processes, strengthen fraud detection, and improve customer experience. You will also design and execute scalable and dependable data processing pipelines, perform analysis, and extract insights to optimize business outcomes. Collaborating with cross-functional teams, you will identify AI/ML application opportunities within the payments ecosystem.
Job Responsibilities
- Actively collaborate with Product, Technology, and other cross-functional teams to deeply understand complex business problems and formulate data-driven solutions in key areas of the payments’ domain.
- Design, develop, and deploy machine learning and AI solutions that achieve 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.
- 3+ years of extensive experience in Natural Language Processing (NLP) or Large Language Models (LLM), or Computer Vision and other machine learning techniques, including classification and 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, DataBricks, Snowflakes.