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 innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes - all while providing customer support to our clients. Our Team: Data AI contributes to the building of Bloomberg's AI-enhanced products at scale by curating model training data and enhancing how our internal processes use AI. By investing in AI at a strategic level, we expand our practice of engaging with AI to one that is embedded across Data. We encourage our internal processes to take advantage of new AI technologies and strengthen Data's role in providing robust domain expertise and influential data artifacts to Bloomberg's products. This way, our clients will continue to have high quality data and access to new types of datasets. The Role: A Senior Data Management Professional (DMP) is a key role within our organization responsible for providing domain 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. The role in the Data AI Annotation team covers all annotation program components in developing the evaluation and training of AI models at Bloomberg. Being responsible for the quality of the annotated data, and product quality will be a crucial part of the role, with key deliverables spanning ownership around consensus management, adjudication, and instruction and task design. The team is a critical partner in ensuring the stability and growth of the company which relies on bringing new technology to our clients with increased interests in Artificial Intelligence. We'll trust you to: Partner with Engineering and Product, providing guidance and direction as we build out, deploy and maintain our AI enhanced products. Applies technical ability to direct a comprehensive and outcome driven data strategy for a given AI project in the context of the annotation datasets Establishes and applies product thinking to data to provide the right capabilities to consumers at the right time Functions as a central resource with other Bloomberg functions (e.g., product, engineering) as the data set(s) move through their lifecycle Directs those involved in the design, modification and evaluation of all phases of a specific data set or group of data sets Defines what a fit-for-purpose data product looks like, and ensures data product health metrics are readily measured and transparently available on an ongoing basis, across both the producer store and consumer interface level You'll need to have: 4 years of professional work experience in Data Product Ownership or related field Strong understanding of client data needs and the strategic landscape within the financial markets Proven experience in data management concepts such as data quality, data modeling, and data engineering Proficiency in being comfortable discussing technical concepts and experience evaluating trade-offs in design with Engineering and Product Extensive experience in communicating and coordinating with internal and external partners while leading large-scale projects Customer-focused approach and the ability to interact with a diverse range of clients Proven ability to take a logical approach and apply critical thinking skills in order to tackle problems Effective project management skills and ability to prioritize tasks accordingly We'd Love to See: Deep subject matter expertise within a chosen financial domain Knowledge of SQL/Python querying language Experience in semantic structures or data modeling Experience using data visualization tools such as Tableau, QlikSense, or Splunk Does this sound like you? Apply if you think we're a good match. We'll get in touch to let you know what the next steps are