JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

As a Data Domain Architect Analyst within JPMorgan Chase & Co., you will work on multiple projects and provide data annotation services across a variety of data types including, but not limited to, text, chats, emails and audio.

Job Responsibilities :

• Work on data labeling tool(s) and annotate data for machine learning models. Sift through structured and unstructured data; identify the right content and annotate with the right label.

• Collaborate with stakeholders including machine learning engineers, data scientists, data engineers and product managers across all of JPMorgan Chase's lines of businesses, such as Investment Bank, Commercial Bank, and Asset Management.

• Work on engagements from understanding the business objective through the data identification, annotation and validation.

• Comprehend the subtleties of language used in the financial industry. Conduct research and bring clarity in business definitions and concepts. Annotate the terms, phrases, and data as per the project requirement.

• Understand and define the relationship among entities. Use programmatic and prompting techniques to fulfill annotation tasks.

• Validate model results from the business perspective and provide feedback for model improvement.

• Effectively communicate data annotation concepts, process and model results to both technical and business audiences. Break down ML annotation topics in a clear manner.

• Adapt to changes in guidelines, priorities and environments

• Transcribe verbatim audio recordings, single and multi-speaker of varying dialects and accents and identify relevant keywords and sentiment labels

Required qualifications, capabilities, and skills

• Strong financial knowledge. Full-time masters in a business management (MBA) with finance specialization.

• Familiarity with industry standard annotation and labeling methods.

• At least 2 years of hands-on experience in data collection, analysis or research and programmatic/prompting techniques to work with text and audio data.

• Should be able to work both individually and collaboratively in teams, in order to achieve project goals.

• Must be curious, hardworking and detail-oriented, and motivated by complex analytical problems and interested in data analytics techniques.

• Interest in Machine learning and should be able to develop

• a working level knowledge on machine learning and LLM concepts

• an understanding of model scoring parameters such as precision, recall and f-score

Preferred qualifications, capabilities, and skills

• Experience in data extraction/collection form financial documents

• Experience with LLM, data annotation, labeling, entity disambiguation and data enrichment.

• Exposure to voice translation services and tools

• Familiarity with Machine learning and AI paradigms such as text classification, entity recognition, information retrieval