The Machine Learning team at JPMorgan Chase applies cutting-edge machine learning techniques to the firm's unique data assets to improve decision-making across lines of business.

As a Data Domain Architect Analyst in the Annotation Center of Excellence (ACoE), you will deliver high-quality annotations across text, chat, email, document, and audio data, producing validated ground-truth datasets for ML/GenAI use cases. You will manage end-to-end annotation work-preparing data, labeling entities and relationships, maintaining taxonomies and guidelines, applying prompt engineering for consistent workflows, and tracking quality metrics-while partnering with ML/data teams and using Python as needed for data prep and QA.

JobResponsibilities :

Own end-to-end annotation engagements: understand business objectives, identify relevant data, annotate, validate, and deliver high-quality ground truth datasets.
Use data labeling tools to annotate structured and unstructured data (e.g., chats, emails, documents, and audio) with high accuracy and consistency. Conduct entity and relationship labeling (including entity linking/disambiguation) and define relationships among entities based on business context.
Applyprompt engineeringto design/test/refine prompts supporting consistent labeling and evaluation workflows.
Apply financial domain knowledge to interpret language nuances and ensure annotations align to agreed business definitions.
Should be conversant withsemantic labeling, define and apply consistent label taxonomies, sub-labels, and hierarchical labeling schemes aligned to use cases.
Transcribe and annotate audio (single/multi-speaker) across dialects/accents, including keyword, intent, and sentiment labeling where required.
Establish and maintain annotation guidelines and standards evolve them as requirements change.
Validate model outputs from a business perspective and provide structured feedback for model improvement. Define and track annotation quality metrics and operational KPIs drive continuous improvement.
UsePython scriptingfor data preparation, sampling, preprocessing, and QA/audit checks (as applicable).

Required qualifications, capabilities, and skills

2+ yearsof post qualification professional experience.
MBA or Master's in finance and/or Data Analytics discipline
Excellent written and oral communication skills to clearly present analytical findings and business recommendations. Highly motivated, productive, and teamwork oriented.
Working-level understanding of ML concepts and evaluation metrics (precision, recall, F1-score) and how data quality impacts model outcomes.
Strong attention to detail ability to work independently and collaboratively in cross-functional teams.
Strong financial domain knowledge and ability to interpret financial language in text and speech.
Experience extracting/collecting data from financial documents and unstructured sources (emails, reports, chat logs).

Preferred qualifications, capabilities, and skills

Familiarity with industry-standard annotation approaches and quality frameworks. Experience with annotation concepts/methodologies (guidelines, QA, taxonomy management, agreement/consistency practices).