Used Tools & Technologies
Not specified
Required Skills & Competences
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.
Python @ 3
SQL @ 3
ETL @ 3
Algorithms @ 3
Data Structures @ 3
Machine Learning @ 3
Communication @ 3
NLP @ 3
LLM @ 3
AI @ 3
- 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.
Details
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 the team does today, with audio, imagery, and video increasingly in scope. Clients include major hedge funds, asset managers, and investment banks using feeds and analytics for low latency and intraday trading, market making, quantitative investing, and risk.
Responsibilities
- 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, and industry events
Requirements
- Bachelor's or graduate degree in business, finance, or an 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 is a plus
- Understanding of data structures, algorithms, machine learning, and quantitative trading
- Good written and verbal communication skills
Preferred / Nice to Have
- 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
Compensation & Benefits
- Salary Range: 140,000 - 295,000 USD Annual
- Benefits may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) with match, life insurance, and various wellness programs
Application
Apply via the company's careers portal (link provided in the original posting).