Product Manager - Enterprise Data

at Bloomberg

📍 New York City, United States

USD 140,000-295,000 per year

MIDDLE

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Algorithms @ 3 Communication @ 3 Data Structures @ 3 ETL @ 3 LLM @ 3 Machine Learning @ 3 NLP @ 3 Python @ 3 SQL @ 3

Details

Enterprise Data at Bloomberg provides machine-readable feeds of news and other unstructured content, as well as AI-powered analytics, including sentiment analysis and more. Text is at the core of the current offering, with audio, imagery, and video increasingly in scope. Clients include major hedge funds, asset managers, and investment banks, which use the feeds and analytics for low-latency and intraday trading, market making, quantitative investing, and risk management.

Responsibilities

Understand client needs and identify improvements and new use cases.
Contribute to 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 a subject matter expert in client discussions, sales meetings, and industry events.

Requirements

Bachelor's or graduate degree in business, finance, or an engineering-related field.
5+ years of work experience in a financial or technology company.
Hands-on experience working with unstructured text data, such as NLP, text analytics, large news or document corpora, or LLM-based pipelines.
Knowledge of Python or SQL. Understanding of ETL pipelines is a plus.
Understanding of data structures, algorithms, machine learning, and quantitative trading.
Good written and verbal communication skills.

Preferred Qualifications

Experience with non-text unstructured data, including 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.
A sense of humor is a plus.

Benefits

Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental, vision, short- and long-term disability benefits, 401(k) matching, life insurance, and wellness programs. Total compensation may also include a bonus.