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Vice President-Generative AI Lead

As a Vice President-Generative AI Lead at JPMorgan Chase, you will enhance, build, and deliver market-leading AI/ML technology products, focusing on secure, stable, and scalable solutions. You will design and architect end-to-end AI solutions, including anomaly detection and GenAI implementations, while collaborating with experts to solve business challenges and generate insights from data analysis.

About the role

About the Role

As a Vice President-Generative AI Lead within the Corporate Technology-Asset Wealth Management Risk Technology team at JPMorgan Chase, you will play a pivotal role in our AI/ML team. You will be instrumental in improving, developing, and delivering trusted market-leading technology products in a secure, stable, and scalable manner. This role involves promoting AI/ML technology solutions across various technical domains to support the firm's business goals, working closely with a team of experts to design and architect comprehensive solutions, proactively tackling significant business challenges, and generating valuable insights from data analysis.

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.

About the role

About the Role

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.