Full Job Description
As a Data Domain Architect Analyst within Consumer and Community Banking, you will leverage your business expertise and knowledge of JPMorgan Chase products to collaborate effectively with Data Science, Analytics, and Engineering teams. You will be responsible for developing and enhancing machine learning solutions by gathering, curating, annotating, enriching, and validating data. Additionally, you will create taxonomies and other resources to train machine learning models, extract insights, conduct analysis, and potentially generate content.
Job Responsibilities:
• Label and annotate call center transcripts and other datasets to support the development of machine learning models.
• Review and validate the outputs of AI and ML models, ensuring accuracy as well as alignment with business goals and compliance standards.
• Leverage GenAI tools to conduct deep dives into call datasets to uncover opportunities to improve customer experience.
• Provide expert guidance to stakeholders on leveraging ML/AI discovery tools for actionable insights.
• Draft and refine taxonomies and classification schemas to enhance data organization and model training.
• Identify and escalate anomalies, errors, or unexpected model behaviors, acting as a critical checkpoint in our AI workflow.
• Work closely with data scientists, engineers, and business stakeholders to continuously improve annotation processes and model performance.
Required qualifications, capabilities, and skills:
• Experience with banking products and/or customer service within the financial services industry
• Bachelor’s degree in business 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 in working with and analyzing large real-world datasets.
• Ability to interpret and apply business context to technical tasks.
• Interest in machine learning and willingness to develop new skills in this area.
Preferred qualifications, capabilities, and skills
• Prior experience with data annotation, conversational analysis, taxonomy development, machine learning projects, and/or quality assurance
• Familiarity with industry annotation and labeling methods.
• Working knowledge of information and data retrieval.
