Data Owner Senior Associate

JPMorgan Chase · Banking · Plano, TX +1 · Consumer & Community Banking

This role focuses on preparing data for AI/ML models within the Consumer and Community Bank (CCB) Operations Data Owner team at JPMorgan Chase. The Data Owner Senior Associate will use Python, LLMs, and RAG to make data AI-ready, design POCs, and collaborate with Engineering and Data Science to productionalize solutions. Experience with predictive models, generative AI, LLM prompt engineering, RAG, Python libraries, ETL pipelines, and evaluation metrics is required.

What you'd actually do

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.

Skills

Required

Python
LLM prompt engineering
Retrieval Augmented Generation (RAG)
Pandas
NumPy
scikit-learn
SQL
ETL data pipelines
Alteryx
evaluation metrics for ML and generative AI
building monitoring dashboards

What the JD emphasized

5+ years' experience in creating predictive models, and generative AI solutions using LLM prompt engineering, Retrieval Augmented Generation (RAG)
Experience with evaluation metrics for ML and generative AI, and with building monitoring dashboards

Other signals

Leveraging AI and ML to make data AI-ready
Leveraging Large Language Models, predictive models and generative AI solutions
Design integrated solutions for AI-readiness of data
Design and demonstrate POCs for making structured and unstructured data AI-ready
Experience with evaluation metrics for ML and generative AI

Read full job description

Are you passionate about leveraging artificial intelligence and machine learning solutions to solve real-world problems?

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.

Job 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.

Required qualifications, capabilities and skills:

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.

Are you passionate about leveraging artificial intelligence and machine learning solutions to solve real-world problems?

Job 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.

Required qualifications, capabilities and skills:

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.