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Data Owner Senior Associate
senior · Data Science & Analytics
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.