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Senior Quant/ML Engineer - Fixed Income - Artificial Intelligence

$165k - $260k

Bloomberg

Senior Quant/ML Engineer - Fixed Income - Artificial Intelligence Location: New York Business Area: Engineering and CTO Ref #: 10051426 Description & Requirements Bloomberg’s Engineering Artificial Intelligence department has 400+ AI practitioners building highly sought after products and features that shape global markets. In our fast-paced Fixed Income domain, you’ll apply mathematics, statistics, machine learning and modern programming frameworks over vast data sources to accurately price millions of securities. You will research methods to offer transparency and accuracy within Fixed Income markets, and you will move Bloomberg’s business forward by assessing, improving, and implementing modern asset pricing models. We are looking for people who are naturally curious, take ownership, are relentless problem solvers, and have the desire to continuously innovate, learn, and grow within the field of Fixed Income and Credit pricing. You’ll Have The Opportunity To Conduct research and analysis to solve complex technical challenges in financial asset pricing within a highly collaborative environment. Conceptualize pricing methodologies, develop and continuously improve mathematical and data-driven models, all while translating algorithms into code. Increase your competency of technical skills as you work on models as research projects, from idea to productionization. Integrate cutting-edge academic and industry research into models and methodologies, staying ahead of emerging developments to drive continuous innovation. Collaborate with cross-functional teams to develop, test, monitor and maintain robust production systems. You’ll Need To Have Ph.D. in the fields of Mathematics, Physics, Statistics, Electrical Engineering, Computer Science, Operations Research, Machine Learning or a similar field from a top tier institution. 3+ years of proven experience on Fixed Income valuation within the buy or sell side. A track record designing, building, evaluating, and maintaining statistical and/or Machine Learning solutions in production is a plus. Experience working with, analyzing and extracting patterns and insights from medium size and large datasets is a must. Proficiency in probability and statistics to support time modeling of financial processes, advanced machine learning, pattern recognition, etc. Strong practical computing skills to support the delivery of statistical and ML models. Experience with object oriented languages is a plus. A creative, rigorous approach to developing and testing novel methods and technologies. A track record of authoring publications in top conferences and journals is a strong plus. We Offer You A flat structure and very collaborative environment of like-minded individuals with strong passion for solving technically challenging problems in an open communication environment. State of the art tech stack to support your day-to-day activities. Tangible growth in skills and impact through the cross-collaboration between AI, Product and technology teams in an open and transparent manner. The ability to work in financial asset pricing in a supportive environment with work-life balance and a casual office environment. Salary Range = 165,000 - 260,000 USD Annual + Benefits + Bonus The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level. We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns. #J-18808-Ljbffr Bloomberg

Vacancy posted 3 days ago
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