Data Owner Senior Associate

JPMorgan Chase & Co.•Plano, TX

19h

About The Position

As a Data Owner Senior Associate, you'll leverage AI and ML to make data AI-ready. You’ll be leveraging Large Language Models, predictive models and generative AI solutions with the Consumer and Community Bank (CCB) Operations Data Owner team. The CCB Data & Analytics team responsibly leverages data from across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

Requirements

Degree in quantitative discipline (e.g., Computer Science, Mathematics, Operations Research, Data Science).
5+ years' experience in creating predictive models, and generative AI solutions using LLM prompt engineering, Retrieval Augmented Generation (RAG).
Strong Proficiency in Python.
Hands-on experience with LLM APIs, Python libraries like Pandas, NumPy, scikit-learn, and others for data manipulation, modeling and analysis.
Proficiency with data table operations (SQL, etc.).
Experience designing, building and maintaining ETL data pipelines using tools such as SQL, Python, and Alteryx.
Experience with evaluation metrics for ML and generative AI, and with building monitoring dashboards.

Responsibilities

Use your programming skills in Python and design integrated solutions for AI-readiness of data. Leverage python libraries, LLMs, and vendor solutions to enable seamless integration of AIML models with business data needs.
Design and demonstrate POCs for making structured and unstructured data AI-ready. Build and iterate on prototype solutions.
Partner with subject matter experts and help deliver solutions that optimize the data for AIML solutions.
Closely collaborate with Engineering and Data Science to productionalize your POCs.
Analyze diverse data assets and sources to prioritize, developing insights that lead to actionable recommendations for sequencing.