Data Domain Architect [Multiple Positions Available]

JPMorgan Chase & Co.•Wilmington, DE

3d•$118,750 - $190,000•Onsite

About The Position

Optimize features and AI capabilities for the Chase Digital Assistant and other conversational AI products. Drive NLU model training and optimization for Chase Digital Assistant (CDA), advancing NLU capabilities and conversational AI understanding for improving digital containment within CDA. Manage intent and entity taxonomy development and align cross-functional teams across Product, Engineering, and Analytics. Optimize training data sets to improve data quality, NLU model F1 score, and intent recognition rate for CDA. Partner with Annotation Lead to review and optimize training data and enable meaningful and measurable outcomes. Design extended analytic frameworks and semantic representations to support NLU models. Conduct conversational analysis to identify systemic improvement opportunities and inform product enhancements. Identify design gaps and systemic improvement opportunities within conversation flows for customer journey optimization and improved completion rate within CDA. Work with Product Managers, ML Engineers, and Analytics by providing linguistic expertise and direction for new NLP capabilities including dialogue, ambiguity, and inference. Identify new testing opportunities and conversational strategies. Develop and maintain documentation on Natural Language Understanding processes, guidelines, and best practices. Guide linguists and conversation analysts to scale annotation initiatives, strengthen evaluation processes, and drive conversational AI excellence.

Requirements

PhD in Computational Linguistics, Linguistics, or related field of study plus 3 years of experience in the job offered or as Data Domain Architect Lead, Computational Linguist, NLU Specialist, Post Doctoral Research Fellow, Siri Lexical Linguist, Linguist, or related occupation.
Designing and optimizing Natural Language Understanding (NLU) and AI-powered systems throughout the product lifecycle, including model training, testing, and evaluation using advanced machine learning and computational linguistics techniques.
Building, testing, and refining language models for AI applications.
Conducting error analysis and quality assurance on datasets to improve model outputs and system reliability.
Creating and maintain taxonomies, and lexical resources to support intent and entity recognition.
Processing and analyzing raw text and datasets to support model development and continuous improvement.
Training, testing, and evaluating model performance using established metrics and methodologies.
Serving as a subject matter expert in linguistics and computational linguistics, resolving intent and entity overlaps within taxonomies.
Reviewing and correcting annotation data, performing error analysis, and addressing bugs by collaborating with annotation teams.
Supporting end-to-end AI product development and deployment utilizing tools and platforms including Notepad++, SpaCy, Python, NLTK, Regex, Bash, Git, and language modeling frameworks.
Tracking project progress and ensuring alignment across cross-functional teams using project management tools including Jira and SharePoint.

Responsibilities

Optimize features and AI capabilities for the Chase Digital Assistant and other conversational AI products.
Drive NLU model training and optimization for Chase Digital Assistant (CDA), advancing NLU capabilities and conversational AI understanding for improving digital containment within CDA.
Manage intent and entity taxonomy development and align cross-functional teams across Product, Engineering, and Analytics.
Optimize training data sets to improve data quality, NLU model F1 score, and intent recognition rate for CDA.
Partner with Annotation Lead to review and optimize training data and enable meaningful and measurable outcomes.
Design extended analytic frameworks and semantic representations to support NLU models.
Conduct conversational analysis to identify systemic improvement opportunities and inform product enhancements.
Identify design gaps and systemic improvement opportunities within conversation flows for customer journey optimization and improved completion rate within CDA.
Work with Product Managers, ML Engineers, and Analytics by providing linguistic expertise and direction for new NLP capabilities including dialogue, ambiguity, and inference.
Identify new testing opportunities and conversational strategies.
Develop and maintain documentation on Natural Language Understanding processes, guidelines, and best practices.
Guide linguists and conversation analysts to scale annotation initiatives, strengthen evaluation processes, and drive conversational AI excellence.