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 and implement technology solutions to enhance our systems, products, and processes.
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. We provide evaluation and annotation frameworks connecting natural language processing and human judgment in order to elevate the quality, intelligence, and usability of the data that drives our products.
By investing in AI at a strategic level, we expand our practice of engaging with AI to one that is embedded across Data. 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. As a result our clients will continue to have high quality data and access to new types of datasets.
What’s The Role:
The Data Product Owner serves as the strategic leader responsible for transforming business operations through data and AI. By aligning product, engineering, and operational partners around a shared vision, they define and deliver the data capabilities that enable intelligent automation, operational scale, and continuous improvement.
This role goes beyond managing requirements to shape how the organization uses data as a strategic asset. Through workflow optimization, process simplification, and the thoughtful application of AI, the Data Product Owner drives solutions that increase efficiency, improve quality, and create sustainable business value.
Success in this role is measured by the organization's ability to operationalize data and AI at scale, turning complex business challenges into streamlined, reliable, and measurable outcomes.
We’ll Trust You To:
- Own and evolve scalable frameworks and sophisticated strategies for instruction and evaluation task design, ensuring datasets remain fit-for-purpose for complex Generative AI behaviors.
- Align data frameworks and evaluation strategies with overarching product objectives to guarantee trustworthy, consumable intelligence that supports actionable user decisions.
- Act as the primary multi-functional liaison, driving alignment between Product, Engineering, and Data teams to translate technical complexities into actionable insights.
- Partner with multi-functional teams to define product-aligned requirements and reusable evaluation rubrics, ensuring outcomes meet rigorous Data Quality standards.
- Drive the strategic evolution of our evaluation infrastructure by pioneering reusable, automated frameworks that consistently accelerate multi-functional product delivery.
You’ll Need to Have:
- Bachelor’s degree or equivalent experience in Finance, Business, Economics, Accounting, STEM or degree-equivalent qualifications
- 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 sophisticated results to partners 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 demonstrable experience working on financial market concepts
- A logical approach to problem-solving with the ability to resolve complex annotation and data-architectural challenges
- Keen interest in and familiarity with generative AI frameworks and the requirements of Agentic AI
- Excellent stakeholder management and project leadership skills, with a demonstrable ability to evaluate design trade-offs and seamlessly translate technical complexities between Engineering, Product, and Data teams.
- Experience in data management concepts such as data quality, data modeling, and data engineering
We’d Love to See:
- DAMA CDMP or DCAM certifications
- 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
- Experience customizing or developing annotation interfaces using Javascript or HTML.