Senior Quant/ML Engineer - Fixed Income - Artificial Intelligence

Bloomberg•New York, NY

11d•$165,000 - $260,000

About The Position

Bloomberg’s Engineering Artificial Intelligence department has more than 400 AI practitioners building products and features for global markets. In the Fixed Income domain, this role applies mathematics, statistics, machine learning, and modern programming frameworks to large data sources to price securities. The position researches methods to improve transparency and accuracy in Fixed Income markets and assesses, improves, and implements asset pricing models. The role involves researching and analyzing technical challenges in financial asset pricing, developing and improving mathematical and data-driven pricing methodologies, and translating algorithms into code. Models are developed as research projects from initial ideas through productionization. The engineer also incorporates academic and industry research and collaborates with cross-functional teams to build, test, monitor, and maintain production systems.

Requirements

Ph.D. in Mathematics, Physics, Statistics, Electrical Engineering, Computer Science, Operations Research, Machine Learning, or a similar field from a top-tier institution.
At least 3 years of experience in Fixed Income valuation on the buy or sell side.
Experience working with, analyzing, and extracting patterns and insights from medium-sized and large datasets.
Proficiency in probability and statistics to support time modeling of financial processes, advanced machine learning, and pattern recognition.
Strong practical computing skills to support delivery of statistical and machine-learning models.
A creative and rigorous approach to developing and testing novel methods and technologies.

Nice To Haves

A track record designing, building, evaluating, and maintaining statistical and/or machine-learning solutions in production.
Experience with object-oriented languages.
A track record of authoring publications in top conferences and journals.

Responsibilities

Conduct research and analysis to solve complex technical challenges in financial asset pricing.
Conceptualize pricing methodologies and develop and continuously improve mathematical and data-driven models.
Translate algorithms into code and take models from research ideas through productionization.
Integrate academic and industry research into models and methodologies.
Collaborate with cross-functional teams to develop, test, monitor, and maintain production systems.