Bloomberg’s core product, the Terminal, is a must-have for the most influential people in finance. In addition to being the second largest producer of news in the world, Bloomberg ingests more than 1.5 million news stories per day from more than 120,000 different sources to help our clients stay in the know. This data would be unmanageable without our help. News stories move markets. We build machines that understand them.

Who are we? Bloomberg's Machine Learning Group - a group of scientists, researchers and software engineers who have a passion for solving complex data problems. We develop applications such as question answering, sentiment analysis of financial news, market impact indicators, social media analysis, topic clustering and classification, recommendation systems, risk analysis and predictive models of market behavior.

Who are you? A research scientist and engineer who wants to apply machine learning to solve challenging open-ended problems. You want to be part of a team making a big impact on the financial industry and are not afraid to get your hands dirty in data.

We'll trust you to:

•  Design and build systems that solve difficult problems involving text, time series and other complex data sources

•  Analyze Bloomberg’s unique data to build novel prediction models

•  Write, test and maintain production-quality C++ and Python code

•  Publish in leading academic venues and represent Bloomberg at industry conferences

You'll need to have:

•  Strong Computer Science fundamentals (algorithms, data structures)

•  Solid background in natural language processing and/or machine learning

•  Industry experience programming in C++ and Python; working knowledge of STL & Boost

•  Strong communications and interpersonal skills

We'd love to see:

•  Strong mathematical background (probability and statistics)

•  A PhD in Machine Learning or Natural Language Processing

•  Publications in top-tier conferences or journals (ACL, EMNLP, ICML, NIPS, KDD)

•  Experience with building machine learning models using time series data

•  Industry experience developing latency sensitive applications

•  Working knowledge of Spark