JPMorgan Vice President-Generative AI Lead 

United States, New Jersey, Jersey City 

195597585

Job Responsibilities:

• Design and architect end-to-end solutions in the AI domain, including anomaly detection use cases, data-driven chat applications, and GenAI implementations.
• Develop a deep understanding of key business problems and processes to drive effective solutions.
• Execute tasks throughout the model development process, including data wrangling, analysis, model training, testing, and selection.
• Generate structured insights from data analysis and modeling exercises, presenting them in formats tailored to various audiences.
• Collaborate with data scientists and machine learning engineers to deploy machine learning solutions.
• Conduct ad-hoc and periodic analysis as required by business stakeholders, the model risk function, and other groups.

Required qualifications, capabilities, and skills:

• At least 5 years of relevant experience post-advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics).
• Experience in statistical inference and experimental design, including probability, linear algebra, and calculus.
• Proficiency in data wrangling, including understanding complex datasets and using Python for cleaning, reshaping, and joining data.
• Practical expertise in both supervised and unsupervised ML projects.
• Strong programming skills in Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R.
• Understanding and usage of the OpenAI API.
• Experience in NLP, including tokenization, embeddings, sentiment analysis, and basic transformers for text-heavy datasets.
• Experience with LLM & Prompt Engineering, including tools like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG).
• Expertise in anomaly detection techniques, algorithms, and applications.
• Excellent problem-solving, communication (verbal and written), and teamwork skills.

Preferred qualifications, capabilities, and skills:

• Experience with deep learning frameworks such as TensorFlow and PyTorch.
• Experience with big data frameworks, with a preference for Databricks.
• Experience with databases, including SQL (Oracle, Aurora), and Vector DB.
• Familiarity with version control systems such as Bitbucket and GitHub.
• Experience with graph analytics and neural networks.
• Experience working with engineering teams to operationalize machine learning models.
• Familiarity with the financial services industry.