Enterprise Data at Bloomberg provides machine-readable feeds of news and other unstructured content, as well as AI-powered analytics, including sentiment and more. Text is at the core of what we do today, with audio, imagery, and video increasingly in scope. Our client base includes major hedge funds, asset managers, and investment banks, typically using our feeds and analytics for low latency and intraday trading, market making, quantitative investing, and risk.

What’s the role?

We’re looking for a charismatic Product Manager to join a growing team of technologists to help drive and execute on product development across our unstructured datasets. You’ll bring with you a few years working in a financial or technology firm in the machine learning/quantitative trading domain, along with some programming and data management skills. You’ll work alongside seasoned industry professionals, gaining personal development whilst contributing your strong technical skills and a positive, can-do attitude.

We’ll trust you to:

• Understand client needs, identify improvements and new use cases

• Contribute to defining product development plans across text and other unstructured datasets

• Drive engineering resources and execute planned development initiatives

• Evaluate new unstructured data sources and assess their quality, coverage, and product potential

• Create and update portfolios of client-facing technical documentation

• Design protocols and tools to facilitate comprehensive product quality checks

• Provide quantitative research to support client testing and onboarding

• Serve as subject matter expert in client discussions, sales meetings, industry events

You’ll need to have:

• Bachelor’s or graduate degree in business, finance, or engineering-related field

• 5+ years’ work experience in a financial or technology company

• Hands-on experience working with unstructured text data - for example NLP, text analytics, large news or document corpora, or LLM-based pipelines

• Knowledge of Python or SQL. Understanding of how ETL pipelines work a plus.

• Understanding of data structures, algorithms, machine learning, quantitative trading.

• Good written and verbal communication skills.

• Sense of humor a plus.

We'd love to see:

• Experience with non-text unstructured data: audio or speech, imagery, or video

• Familiarity with multimodal machine learning, embeddings, or modern foundation models

• Experience building or evaluating data labeling, annotation, or quality pipelines at scale