Machine Learning Quant - Fixed Income Pricing Machine Learning Quant - Fixed Income Pricing …

Bloomberg
in New York, NY, United States
Internships & Graduate Trainee, Full time
Last application, 12 Aug 20
Competitive
Bloomberg
in New York, NY, United States
Internships & Graduate Trainee, Full time
Last application, 12 Aug 20
Competitive
Machine Learning Quant - Fixed Income Pricing
The bond market is a trillion-dollar industry, twice the size of the stock market. We provide pricing on tens of thousands of bonds issued by governments and corporations. Our pricing provides bond traders with an accurate reference price even if there is no central exchange for trading these bonds. 
 
By bringing transparency to an otherwise opaque market, our impact is significant. Our pricing is viewed world-wide by bond traders and central banks to see how markets are responding to government and corporate announcements. 
 
Our pricing engines ingest billions of incoming market quotes per day and run them through pricing models to price over 3 million securities a day. 
 
We'll trust you to
  • Drive projects via conceptual planning and design of solutions to open-ended problems by leveraging domain expertise often under aggressive deadlines
  • Prototype and implement pricing models
  • Collaborate with peers on code reviews, integration with system level software and production rollout process
  • Analyze market data to identify signals to help build models that achieve business goals
  • Review and improve quality and performance of pricing models on a continual basis

You'll need to have
  • Ph.D. (or MS with 2+ years of industry experience) in AI/ML, statistics, engineering or related field
  • Proficiency in Python programming
  • Experience with deep learning frameworks such as PyTorch, Tensorflow and a distributed computing framework like Spark.
  • Some exposure or experience in the following or related areas of finance is highly desired: Fixed income mathematics, Market microstructure, Quantitative trading strategies.

Close
Loading...