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.

What's the Role?

The role of a Data Product Owner in the Data AI team is to build the strategic evaluation data intelligence layer roadmap. The role moves beyond tactical annotations to act as the primary architect of cross-functional trust, connecting cross-functional teams, tools and content owners around shared data goals The mission is to transform our evaluation data collection processes into scaled pipelines, ensuring that the annotated data serves as a high-precision asset for training and evaluating Generative AI products. You will be accountable for the integrity and product alignment of the evaluation framework.

We’ll trust you to:

● Align data frameworks and evaluation strategies with overarching product objectives to ensure outputs produce trustworthy, consumable intelligence.

● Drive alignment between stakeholders to ensure datasets deliver actionable insights and support specific user decisions.

● Ensure the integrity of outcomes and vouch for high-quality signals.

● Identify opportunities to modularize, automate, and orchestrate workflows, moving from one-off projects to a reusable operational layer.

● Create sophisticated strategies for instruction and evaluation task design, ensuring datasets are fit-for-purpose for complex AI behaviors.

● Establish and safeguard standard processes in data annotation, promoting the reuse of frameworks across different domains and products while proactively collaborating with data quality, data engineering and annotation operations colleagues to ensure operational excellence.

You’ll need to have:

● A bachelor’s degree or above in Statistics, Data Analytics and Data Science or other STEM related fields.

● A minimum of four years of demonstrated experience in data management concepts, including data quality, modeling, and random sampling.

● Extensive experience using data visualization tools such as Tableau or Qlik Sense to communicate complex results to stakeholders in a clear, concise manner.

● Demonstrable experience in Data Profiling/Analysis using tools such as Python, R, or SQL.

● Past project/experience analyzing financial datasets or proven experience working on financial market concepts.

● A logical approach to problem-solving with the ability to resolve complex annotation and data-architectural challenges.

We’d Love to See:

● DAMA CDMP or DCAM certifications.

● Keen interest in and familiarity with generative AI frameworks and the requirements of Agentic AI.

● Experience in using Bloomberg Data, Bloomberg Terminal, and/or enterprise financial data products.

● Interest in solving problems and developing data-driven methodologies for high precision & high recall anomaly detection.

● Past project experience using Agile/Scrum methodologies to manage complex data lifecycles.

Does this sound like you?

Apply if you think we're a good match. We'll get in touch to let you know next steps!