The Derivatives Data quant group is primarily responsible for building Bloomberg reference data (curves, volatility surfaces, volatility cubes) across all asset classes (equity, commodity, credit, FX, interest rates and inflation) out of various listed and OTC traded instruments.

As a quant intern working closely with senior quants, we'll trust you to:

• Devise metrics for the analysis, scoring and quality assessment of input market data and generated reference data
• Employ statistical techniques to predict missing data for illiquid entities
• Apply machine learning algorithms to identify proxies and peer groups among observable market data
• Implement the above in flexible tools for use by the quant group as well as other internal and external clients

You will have the opportunity to sharpen your derivatives math skills, while gaining exposure to practical aspects of multiple asset classes, and addressing real-world computational problems. You will be part of a team that builds industrial-strength implementations and cutting edge products that have a big impact on the bottom line of our clients. The internship will also allow you to gain practical experience working in a state-of-the-art Linux Spark cluster environment

You’ll need to have:

• Pursuing a Masters/PhD in a technical area such as Math, Physics, Engineering, Data Science or Machine Learning
• Strong knowledge of a variety of data analysis, statistical inference and other numerical techniques
• Experience in big data algorithms
• Ability to encode these methodologies into high-performance implementations within real-time applications
• Strong programming skills in Python/C++
• Some experience with Spark, PySpark and IPython
• A basic knowledge of derivatives models in any asset class

If this sounds like you:

Apply if you think we're a good match. We'll get in touch to let you know what the next steps are, but in the meantime feel free to have a look at this: http://www.bloomberg.com/professional