Bloomberg's Machine Learning Group, is 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.

We are looking for an expert research scientist and engineer who wants to apply machine learning to solve challenging open-ended problems. You will 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 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

If this sounds like you:

Apply if you think we're a good match! We'll get in touch with you to let you know what the next steps are. In the meantime, check us out at http://www.techatbloomberg.com/

Bloomberg is an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.