Data Annotation Lead

Shape how high-quality data powers next-generation analytics and artificial intelligence in financial services. Join a team where your leadership and domain expertise directly improve model performance and business decision-making. Build a long-term career with access to mobility, learning, and the scale of diverse, high-impact use cases.

As a Data Annotation Lead at JPMorgan Chase, you will lead a data annotation team that produces reliable labeled datasets used to train and evaluate machine learning and generative artificial intelligence solutions. You will own delivery, quality controls, and stakeholder alignment across multiple annotation engagements. You will translate business objectives into annotation guidelines, taxonomies, and validation methods that create dependable ground truth.

In this role, you will partner closely with data science, analytics, engineering, and product stakeholders to define what “good” looks like for labeled data and ensure teams can use it confidently in production workflows. You will guide annotation strategy across multiple data types such as text, chat, email, documents, and audio. You will balance speed and rigor by using clear standards, measurable quality metrics, and continuous improvement practices. You will also help the team adopt workflow automation and human-in-the-loop approaches that improve throughput while protecting data quality.

• Own end-to-end delivery of multiple data annotation engagements, including scope, timeline, staffing, and final acceptance of outputs.
• Lead and coach a team of domain expert annotators; set expectations, review work quality, and provide ongoing performance feedback while contributing hands-on when needed.
• Define annotation guidelines, labeling instructions, and gold-standard examples that enable consistent decisions across annotators and projects.
• Develop and maintain label taxonomies, sub-labels, and hierarchical structures aligned to business definitions and model evaluation needs.
• Validate labeled data and model outputs from a business perspective; document issues and provide structured feedback to improve model performance and data requirements.
• Measure annotation quality and operational performance using clear metrics; drive root-cause analysis and continuous improvement actions.
• Partner with engineering and data teams to support data preparation, sampling, and validation checks for structured and unstructured datasets.
• Facilitate effective use of labeling tools and workflows across data types (for example, chat, email, documents, and audio) with a focus on accuracy and consistency.
• Implement prompt-based and human-in-the-loop workflows that improve labeling consistency and quality review efficiency, aligned to governance expectations.

• 12+ years of professional experience in financial services, data operations, analytics, or related roles.
• People management experience leading and developing teams delivering operational and/or data outcomes.
• Demonstrated experience delivering data annotation or data labeling workstreams end-to-end (scope, guidelines, execution, quality controls, and delivery).
• Demonstrated financial domain knowledge interpreting financial language in text and/or speech for consistent labeling decisions.
• Working understanding of machine learning concepts and evaluation measures (for example, precision, recall, and F1 score) and how data quality affects outcomes.
• Experience defining or managing labeling guidelines, taxonomy governance, and consistency practices (for example, reviewer workflows or agreement checks).
• Working proficiency in Python for data preparation and basic quality or audit checks.
• Experience designing, testing, and refining prompts to support consistent labeling and evaluation workflows.
• Ability to manage multiple concurrent projects with clear prioritization, stakeholder communication, and delivery discipline.
• Strong attention to detail with the ability to document decisions and maintain traceable standards.

• Experience labeling or extracting information from unstructured financial sources such as emails, reports, or chat logs.
• Familiarity with natural language processing tasks such as text classification, entity recognition, entity linking or disambiguation, and information retrieval.
• Exposure to speech transcription workflows, including single- and multi-speaker audio and varied accents or dialects.
• Familiarity with industry approaches to annotation quality frameworks and operational quality measurement.