About the job
As the scale and strategic importance of AI-powered products at Bloomberg continues to grow, we are seeking a strategic leader to oversee the teams responsible for producing high-quality training and evaluation data that power Bloomberg’s structured intelligence systems. This organization plays a foundational role in ensuring that structured representations used by Bloomberg’s AI systems are accurately modeled, consistently annotated, and rigorously evaluated. The role is responsible for defining standards for intent modeling, schema alignment, annotation quality, and evaluation methodologies, ensuring precision, consistency, and domain fidelity across structured AI workflows.
Responsibilities
Lead and scale a technically fluent organization responsible for producing high-quality training and evaluation datasets that power Bloomberg’s structured AI systems, while hiring, mentoring, and developing the next generation of technical leaders.
Establish clear standards, governance frameworks, and performance metrics to ensure consistent, high-quality data production at scale.
Partner closely with Engineering, Product, and AI stakeholders to align data design and evaluation methodologies with modeling approaches and production requirements.
Guide teams in translating complex financial expertise into precise, high-fidelity structured representations suitable for advanced AI applications.
Drive improvements in tooling, automation, and operational workflows to increase data quality, scalability, and organizational leverage.
Operate as a strategic leader, balancing near-term delivery with long-term capability building and platform maturity.
Qualifications
Minimum
8+ years of experience leading AI/ML data, annotation, or evaluation organizations, including significant people leadership responsibility.
Demonstrated success building and operating large-scale data production functions that support machine learning systems in production environments.
Strong technical fluency, with the ability to engage credibly with engineering and AI partners on how data design, structure, and quality influence model behavior and system performance.
Deep understanding of generative AI workflows and the role rigorous training and evaluation data play in ensuring reliability and domain accuracy.
Proven ability to develop technical talent and elevate team sophistication in structured data design and evaluation practices.
Strong analytical judgment and leadership presence, with the ability to balance long-term strategy and near-term execution while communicating complex ideas clearly across stakeholders.
Preferred
Advanced degree in Computer Science, Engineering, Data Science, or a related field.
Experience operating in highly technical, ML-adjacent, or data-intensive environments.
Background in financial services or similarly complex, domain-rich industries.
Familiarity with modern annotation platforms, data tooling, and quality systems.
Prior hands-on experience in technical or engineering contexts.