Machine Learning and Artificial Intelligence play a critical role in transforming  Operations. The ability to utilize data in meaningful ways allows us to develop solutions which both our customers and employees can benefit from. Customers expect tailored servicing and Chase is looking to deliver personalization to meet their needs. This is powered by high-quality annotated data and detailed annotation schemes that are the backbone of impactful algorithms and applications.

As a Data Domain Architect Analyst, you will use your domain expertise to partner closely with teams in Data Science, Analytics and Engineering to develop machine learning solutions. This will involve the collection, curation, annotation, enrichment and validation of data and the development of taxonomies and other linguistic resources to help train machine learning models, drive insight, analysis, and possible content creation.

 Job responsibilities

Support data annotation and label data/content using annotation tools and analysis. Implement annotation procedures to achieve consistent annotation. Report and document issues with annotation tools and processes. Provide feedback for efficient labeling and to improve data quality. Conduct analysis of annotation edge cases. Contribute to the development of taxonomies and annotation guidelines. Identify patterns and trends in conversational data through Natural Language Processing and/or other computational linguistic approaches. Actively contribute the team’s continuous learning mindset by bringing in new ideas and perspectives that stretch the thinking of the group.

 Required qualifications, capabilities, and skills

Bachelor’s degree or higher in Linguistics or comparable discipline; or equivalent level demonstrated in relevant work experience. Excellent analytical and problem-solving skills and the ability to pay close attention to detail Experience using Python in working with and analyzing large real-world datasets. Working knowledge of information and data retrieval. Working knowledge of machine learning and artificial intelligence paradigms and libraries. Interest in machine learning and willingness to develop new skills in this area. Experience in dialog models, conversational analysis, or other data science projects Familiarity with industry annotation and labeling methods. Familiarity with Finance and Banking products.