Data Domain Architect Lead
Quick summary
- Work type
- On-site
- Location
- Wilmington, DE
- Posted
- 33 days ago
- Nearby
- 99+ roles within 25 mi
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has 439 open roles on FindRole.
Listed pay typically runs $152,000–$215,000 across 233 roles with salary data.
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At a glance
TL;DR · Data Domain Architect Lead
As a Data Domain Architect Lead within Chase’s Consumer and Community Banking Operations team, you will leverage your expertise to manage and coach a team of Machine Learning Data Domain analysts in the annotation and labeling of data using advanced tools. Your role involves collaborating closely with Data Science, Engineering, and Analytics teams to develop strategies for optimizing training data for machine learning models, identifying patterns through Natural Language Processing techniques, and evaluating model outputs. You will work extensively with Python, SQL, and other data technologies such as Teradata and Oracle databases, while also having a strong understanding of large language models and prompt engineering. This position requires experience in finance and banking products, along with expertise in data modeling, machine learning algorithms, and analytical tools to drive impactful AI solutions at scale.
Skills
What you'll do
- Manage and coach a team to support data annotation and label content using tools.
- Develop strategies with Data Science teams to optimize training data for ML models.
- Lead efforts in identifying patterns in conversational data through NLP approaches.
- Evaluate the quality of machine learning classification outputs with stakeholders.
- Contribute new ideas to foster continuous learning and innovation within the team.
What we're looking for
- 6+ years experience in developing machine learning solutions.
- Expertise in data modeling techniques, tools, and unstructured data analysis.
- Familiarity with finance and banking products and industry annotation methods.
- Proficiency in Python for large dataset analysis and machine learning libraries.
- Strong analytical skills and attention to detail for high-quality data curation.