About Us

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 JPMorgan 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 an Associate 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.


Job Responsibilities :

• 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.
• Apply prompt engineering to 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 with semantic 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.
• Partner with ML engineers, data scientists, data engineers, and product partners across lines of business; communicate clearly to technical and business audiences.
• Use Python scripting for data preparation, sampling, preprocessing, and QA/audit checks (as applicable).

Required qualifications, capabilities, and skills

• 6+ years of post qualification professional experience.
• MBA or Master s in finance and/or Data Analytics discipline

Preferred qualifications, capabilities, and skills

• Excellent written and oral communication skills to clearly present analytical findings and business recommendations. Highly motivated, productive, and teamwork oriented.
• 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).
• Familiarity with industry-standard annotation approaches and quality frameworks. Experience with annotation concepts/methodologies (guidelines, QA, taxonomy management, agreement/consistency practices).
• 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.