Bloomberg's Engineering AI department needs to design and implement advanced models using modern ML and statistical techniques on novel technology stacks and vast data sources to accurately price millions of securities in the fast-paced fixed income domain. The goal is to expand the group's capabilities to tackle more ambitious challenges in fixed income modeling and contribute novel modeling ideas to production-quality code for cloud-native environments.
Requirements
- 3+ years of relevant work experience with Machine Learning or Statistical Modeling techniques in the financial industry ideally around asset valuation.
- Proven track record designing, building, evaluating and maintaining statistical or Machine Learning solutions on Production.
- Proficiency in software engineering with an understanding of Computer Science fundamentals such as data structures and algorithms.
- Expertise in Fixed Income modeling, interest rate theory, credit risk, or advanced statistical/machine learning techniques.
- A track record of authoring publications in top conferences and journals is a strong plus.
Responsibilities
- Develop, 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 approaches to power the pricing capabilities of Bloomberg.
- Integrate cutting-edge academic and industry research into models and methodologies, staying ahead of emerging developments to drive continuous innovations.
- Represent Bloomberg at scientific and industry conferences, and publish research findings through documentation, whitepapers, or in leading academic journals and conferences.
- Write clean, modular, production-quality code for cloud-native environments.
Other
- 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.
- Excellent communication skills and the ability to collaborate with engineering peers as well as non-engineering stakeholders.
