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-driven 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 News Content Indexing, supporting Natural Language Processing (NLP) enrichments across multiple news platforms. These enrichments are critical for managing the automated classification of the world's most meaningful financial and economic news, with the aim of further entrenching Bloomberg as the leader in the financial news market.

Our team builds and manages scalable annotation frameworks, driving quality training and evaluation datasets. We partner closely with News Product and Engineering to elevate the performance of Machine Learning (ML) models, enrichments, and features delivered to clients. Looking forward, our roadmap includes the expansion of our coverage universe and support of further enhancements to Bloomberg’s News summarization offerings.

What’s The Role:

A Senior Data Management Professional (DMP) is a key role within our organization responsible for providing subject matter expertise in both financial concepts and annotation program management, to the development of our AI products. These individuals act as proactive technical leaders by setting the framework in achieving quality and consistency in the evaluation and training datasets for models that power our AI-enhanced products, and delivering scalable governance in annotation program management across Bloomberg Data. Beyond governing data processes and being problem solvers, they are expected to transform the responsibilities of the team and scale the impact beyond what's possible today.

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, text summarization, and classification enrichments, enabling fast, relevant, and trustworthy information delivery. You'll be expected to guide the data design that supports 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.

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.

● Apply problem-solving and critical thinking, with a focus on innovation and continuous improvement to our news classification taggers.

● Design and manage annotation programs for news enrichments

● Develop scalable strategies for topic and entity classification, tailored for NLP enrichments across news products.

● Shape and evolve schematic structures and data models that serve as the foundation for annotation quality and reuse.

● Incorporate machine learning and statistics to detect anomalies and drive quality improvement in areas such as accuracy, completeness, consistency, and reliability

● Collaborate with AI 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.

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 professional work experience

● Proven experience with development of data annotation programs and human-in-the-loop processes for AI/ML products

● Proven experience in data management concepts such as data quality, data modeling, and data engineering

● Familiarity with search infrastructure, classification 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.

● Extensive experience in communicating and coordinating with internal and external partners while leading large-scale projects.

● Experience customizing or developing annotations interfaces.

● Experience using data visualization tools such as Tableau, Qliksense, or PowerBI

● Customer-focused approach and the ability to interact with a diverse range of clients.

● Effective project management skills and ability to prioritize tasks accordingly.

● Proficiency in discussing technical concepts and experience with evaluating trade-offs in design with Engineering, Product, and broader data annotation team

● Proven ability to take a logical approach and apply critical thinking skills in order to tackle problems.

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.).

● Experience using native language skills to capture various forms of linguistic expression with high accuracy

● Experience with sophisticated data annotation initiatives at significant scale

● Background in information retrieval, semantic search, or abstractive summarization.

● Familiarity with model lifecycle practices (training, fine-tuning, evaluation).

● Experience customizing or developing annotation interfaces using Javascript or HTML.

● Certification in data governance (e.g., DAMA CDMP, DCAM).