Senior Data Management Professional - Annotation Program Management

Bloomberg-posted 3 months ago
$110,000 - $190,000/Yr
New York, NY
5,001-10,000 employees
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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. 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.

  • 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.
  • 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.
  • 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).
  • Medical, dental, vision insurance
  • Short and long term disability benefits
  • 401(k) with match
  • Life insurance
  • Various wellness programs
  • Paid holidays
  • Paid time off
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