Team Leader – Document Research

Overview

In this role you lead the Document Research team within Bloomberg’s Data AI group, shaping evaluation and annotation programs for AI-driven products. You will partner with Engineering, CTO and Product to align data management with product goals and ensure data quality at scale. Your work spans multiple domains, guiding data governance and measurement to improve model behavior and evaluation outcomes. This is a hands-on leadership role with impact on enterprise-wide AI initiatives and data standards.

Responsibilities

• Lead development of evaluation and annotation programs for AI products within Document Research
• Represent Data in product development alongside CTO, Engineering and Product
• Provide domain expertise to inform evaluation frameworks and annotation methodologies
• Scale data operations and ensure data quality through standards, drift detection and measurement
• Mentor and grow a team of data management professionals focused on evaluation, annotation, and project management
• Drive improvements to schemas, workflows, and operational processes across teams
• Contribute to technical discussions including design and code reviews while aligning with stakeholders
• Collaborate with strategic partners on enterprise-wide programs with governance and cross-team alignment

Key requirements

• Bachelor’s or Master’s degree
• 3+ years leading operational or program-focused teams
• Strong technical judgment in annotation systems supporting ML training/evaluation
• Good understanding of annotation systems, calibration, agreement modeling and drift detection
• Experience running large-scale annotation or data operations with vendor/SME workforces
• Ability to enforce centralized standards at scale
• Excellent cross-functional leadership and comfort with ambiguity
• Strong program leadership with focus on measurable outcomes and continuous improvement

• Cross-functional collaboration
• Adaptability in ambiguity-rich environments
• Mentorship and people development
• Annotation systems design and quality methodologies
• Drift detection and calibration
• Data governance and workflows

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