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.