Team Leader - Annotations Operations and Governance

Bloomberg•Princeton, FL

2d•$135,000 - $230,000•Onsite

About The Position

The Bloomberg Data AI group brings innovative AI technologies into Bloomberg’s Data organization while contributing deep financial domain expertise to the development of AI-powered products. We partner closely with stakeholders to align AI innovation with Bloomberg’s strategic objectives, focusing on optimizing data workflows and elevating the quality, intelligence, and usability of the data that drives our products. Our work amplifies the impact of the Data organization by delivering intelligent data solutions and domain-informed systems that enhance the capabilities and competitiveness of Bloomberg’s offerings. We are seeking a Team Lead to own and scale Bloomberg’s annotation function across AI and data initiatives, with responsibility for Annotation Operations and Annotation Standards & Governance. This role leads two tightly coupled but distinct capabilities: (1) governing canonical annotation standards and judgment frameworks, and (2) applying those standards at scale through operational execution, quality control, and continuous improvement. The Team Lead will operate annotation as a shared service across our Data AI teams. They will ensure that centrally defined standards: schemas, labels, ambiguity frameworks, and calibration rules, are consistently and correctly applied in production through SME-driven workflows, vendor execution, and robust operational controls. The ideal candidate is a technically grounded, systems-oriented leader who understands how annotated data shapes AI model behavior and evaluation outcomes. You are comfortable operating at scale while exercising strong technical judgment, enforcing standards, interpreting ambiguity, and using metrics to detect drift, diagnose failure modes, and continuously improve data quality. In this role, you will help build a durable, repeatable annotation capability that produces correct, consistent, and reproducible data over time, supporting training, evaluation, monitoring, and production AI systems.

Requirements

Prior people leadership experience, including leading operational or program-focused teams.
Demonstrated technical judgment in designing or operating annotation systems that support machine learning training, evaluation, or model assessment.
Strong understanding of annotation systems and quality methodologies, including calibration, agreement modeling, and drift detection.
Proven experience running large-scale annotation or data operations with vendor and SME workforces.
Ability to enforce centrally defined standards while maintaining consistency at scale.
Excellent cross-functional leadership skills and comfort operating in ambiguity-rich environments.
Strong program leadership capability, with a focus on measurable outcomes and continuous improvement.
Bachelor’s or Master’s degree in a relevant field, or equivalent practical experience.

Nice To Haves

Experience supporting ML training, evaluation, or monitoring pipelines.
Familiarity with annotation platforms, QA tooling, and data instrumentation.

Responsibilities

Lead and develop a team responsible for operating and governing Bloomberg’s AI data annotation capability, delivering consistent, high-quality data through strong technical judgment and clear standards.
Own annotation operations end-to-end, translating schemas and judgment frameworks into scalable workflows with measurable quality.
Establish governance and quality mechanisms, including calibration, agreement analysis, and drift detection, that ensure consistent interpretation of standards.
Partner closely with AI, Data, and Platform teams to align annotation outputs with production needs and downstream model requirements.
Ensure operational strength at scale, including workforce strategy, vendor oversight, capacity planning, and service reliability.
Drive continuous improvement through metrics, feedback loops, and root-cause analysis.
Act as steward of judgment integrity, maintaining high agreement and durable decision-making frameworks as models and domains evolve.