Derivatives Quant Summer Intern 2020 Derivatives Quant Summer Intern 2020 …

Bloomberg
in New York, NY, United States
Internships & Graduate Trainee, Full time
Last application, 23 Feb 20
Competitive
Bloomberg
in New York, NY, United States
Internships & Graduate Trainee, Full time
Last application, 23 Feb 20
Competitive
Derivatives Quant Summer Intern 2020
Bloomberg's Quantitative Library team is responsible for the full life cycle of researching, developing and maintaining quantitative pricing libraries that power Bloomberg's derivatives pricing models and supports its risk management and derivatives valuation services.

The team is looking for an Intern with interest and experience in machine learning, especially in application of derivative pricing. In this role, you will have the opportunity to explore machine learning techniques to solve numerical problems in derivatives pricing.
Practical knowledge of numerical solution of PDE, or Monte-Carlo is a plus, but by no means required.
 
We'll trust you to:
  • Come up to speed on machine learning
  • Research on ways to apply machine learning techniques to improve or extend pricing library
  • Work independently or in collaboration with your team members
  •  
    You'll need to:
  • Have 1-2 years (academic or professional) experience in machine learning
  • Be working toward a MS or PhD in Machine Learning, Electrical Engineering, Computer Science, Math or related field
  • Be able to learn on the job
  • Be available to work the whole duration of 10 weeks
  •  
    We'd love to see:
  • Extensive experience with modern tools used in machine learning community
  • Experience with numerical methods
  • Familiarity with derivatives pricing models

If this sounds like you:
Apply if you think we're a good match and we'll get in touch with you to let you know next steps. In the meantime, check out http://www.bloomberg.com/professional .

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

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