Senior ML Quant Engineer - Fixed Income - Artificial Intelligence

Bloomberg's Engineering AI department has 350+ AI practitioners building highly sought after products and features that shape global markets. In our fast-paced Fixed Income domain, you'll design and implement advanced models that leverage modern ML and statistical techniques on top of novel technology stacks and vast data sources to accurately price millions of securities. We are heavily invested in data-driven solutions that combine statistical and machine learning solutions to price diverse asset classes, with a strong focus on Fixed Income. Building on the success of modern ML-based pricing solutions, we are expanding our group to tackle more ambitious challenges in Fixed Income modeling. In this role, you will contribute novel modeling ideas and bring them to life by writing clean, modular, production-quality code for cloud-native environments, ensuring your work makes a tangible impact.

We seek highly multifaceted skilled individuals with expertise in Fixed Income modeling, interest rate theory, credit risk, or advanced statistical/machine learning techniques.

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• Design, build and evaluate statistical and Machine Learning models that directly influence how global markets price fixed income assets
  
• Collaborate with cross-functional teams to develop, test, monitor and maintain robust production systems.
  
• Design new architectures, systems and tools to power next-generation pricing capabilities of Bloomberg.
  
• Integrate cutting-edge academic and industry research into models and methodologies, staying ahead of emerging developments to drive continuous innovation.
  
• Represent Bloomberg at scientific and industry conferences, and publish research findings through documentation, whitepapers, or in leading academic journals and conferences.
  

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• Previous relevant work experience with Machine Learning or Statistical Modeling techniques in the financial industry, ideally around asset valuation. A track record designing, building, evaluating, and maintaining statistical or Machine Learning solutions in production is a plus.
  
• Ph.D. or M.Sc. with equivalent research experience in Machine Learning, Computer Science, Mathematics, Statistics or a related field.
  
• Thriving in solving challenging, often ill-defined problems where off-the-shelf solutions fall short, and bring a creative, rigorous approach to developing novel methods and technologies.
  
• Proficiency in software engineering with an understanding of Computer Science fundamentals such as data structures and algorithms.
  
• Excellent communication skills and the ability to collaborate with engineering peers as well as non-engineering stakeholders.
  
• A track record of authoring publications in top conferences and journals is a strong plus.
  

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