Job Description

Senior Data Management Professional - Data Product Owner - Annotation Management and Dialogue Labeling
Location
London
Business Area
Data
Ref #
10046037

Description & Requirements

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Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock from around the world. In Data, we are responsible for delivering this data, news, and analytics through innovative technology, quickly and accurately. We apply problem-solving skills to identify workflow efficiencies, implement technology solutions to enhance our systems, products, and processes, and provide support to our clients.


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Our Team

The Bloomberg Data AI group brings cutting-edge AI technologies into Bloomberg's Data organization, supplying deep domain expertise to the development of AI-powered products. One of our core areas focuses on Dialogue Labeling and Annotation Management, supporting Natural Language Processing (NLP) enrichments across multiple communication platforms. These enrichments are critical for structuring unstructured client communications and fueling intelligent downstream search, classification, summarization, and insight generation.



Our team builds and manages scalable annotation frameworks, driving quality training and evaluation datasets. We partner closely with Product and Engineering to elevate the performance of Machine Learning (ML) models, enrichments, and features delivered to clients. Looking forward, our roadmap includes expansion into communication-based search insights and further enhancements of ML enrichments.

What's The Role

As a Data Product Owner, you are a true hybrid professional - a bridge between deep financial domain expertise and cutting-edge technology. You will play a pivotal role in solving complex data challenges in finance by ensuring our data collection, documentation, and tooling adhere to MDLC (Machine Learning Development Lifecycle) best practices.



This role requires a strategic mindset around ML training data design, a strong understanding of data modeling and schema architecture, and the ability to align data strategies with product objectives. You will also be responsible for designing data pipelines and annotation schemas that support search relevance modeling, query understanding, and text summarization, enabling fast, relevant, and trustworthy information delivery from communication streams. You'll be expected to guide the data design that supports extracting intent, identifying salient content, and generating concise responses or insights that drive decision-making for Bloomberg clients. You may also be responsible for providing support for our NLP solutions in other domains involving complex financial instruments.

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We'll Trust You To

Own the end-to-end Annotation Lifecycle

, from schema development to annotation execution, with an eye toward ML performance and product utility.

Bridge the gap between finance and AI/ML

by mastering domain-specific concepts that elevate communications experiences.

Design and manage annotation programs

for search and summarization use cases, including training data for relevance ranking, query-document matching, and text abstraction.

Develop scalable strategies

for data labeling and dialogue annotation, tailored for NLP enrichments across communication products.

Shape and evolve schematic structures and data models

that serve as the foundation for annotation quality and reuse.

Define metadata structures and enrichment tags

that help interpret communication context, intent, and relevance to user queries.

Collaborate with ML engineers and product stakeholders

to align annotation efforts with model requirements and product goals.

Drive quality and consistency

across annotation processes by developing clear guidelines, validation metrics, and governance frameworks.

Leverage insights and analytics

to iterate on annotation strategies and measure downstream model and product impact.

Lead efforts to improve annotation throughput, coverage, and enrichment scope

by identifying automation and optimization opportunities.

Stay current on trends

in search technologies, summarization architectures, and best practices for building reliable training datasets in these domains.

Serve as a domain expert

in data structuring, labeling, and ML data design within communications-focused NLP use cases.


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You'll Need to Have

Please note we use years of experience as a guide, but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role.

4+ years of experience

working in

AI/ML data roles

, ideally focused on NLP, communications, or information extraction. Proven experience with annotation programs, dialogue labeling, or large-scale training/evaluation dataset development.

Strong grasp of data modeling

, schema design, and best practices for structuring unstructured data. Familiarity with search infrastructure and summarization models, and how data influences relevance ranking and response generation.

Demonstrated ability to design, scale, and govern data pipelines

that support high-impact ML model training and evaluation.

Comfort engaging with ML practitioners

to co-design data schemas and evaluate performance trade-offs. Excellent project management skills and the ability to manage competing priorities across multiple stakeholders.


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We'd Love to See

Knowledge of Python, SQL, and common ML/NLP tooling. Experience working with annotation tools or platforms (e.g., Prodigy, Labelbox, Snorkel, etc.). Background in information retrieval, semantic search, or abstractive summarization. Familiarity with model lifecycle practices (training, fine-tuning, evaluation). Experience with generative AI systems and prompt evaluation workflows. * Certification in data governance (e.g., DAMA CDMP, DCAM).