Quant Analyst

3 days ago

New York NY, United States myGwork Full time
This job is with Bloomberg, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

Bloomberg’s Quantitative Analytics team is responsible for the design and implementation of modeling analytics that support client pricing and risk management solutions for financial products across the entire suite of Bloomberg products and services, including its terminal with 300,000+ clients, trading system solutions, buy and sell-side enterprise risk management, and derivatives valuation services. These models include those for pricing and risk of derivative products across all major asset classes, including market data; counterparty credit risk, XVA, initial margin; value-at-risk and other market risk metrics; climate risk; credit risk and liquidity risk. The team has two recent Risk Quant of the Year winners and is dedicated both to novel research as well as efficient model delivery through modern C++ and Python libraries. . Within the Quantitative Analytics team, the Quantitative Risk Analytics group (“QRA”) is responsible for all credit, climate, market, and liquidity risk related modelling. Our Liquidity Risk models provide estimates on the available volume, cost to sell, and time to sell for a wide range corporate, municipal and sovereign debt, and equity securities under current market conditions and stress scenarios. The group is responsible for model research and development, as well as model deployment into production in collaboration with our Model Validation, Engineering, and Product Manager partners.

The Quantitative Risk Analytics group has an open position in New York for an experienced Liquidity Risk Quantitative Analyst to support our growing client business. The candidate will be responsible for researching, and prototyping models, documenting models, planning project execution, and coordinating with team members. We will trust you to: -Research, design, prototype, implement, test, document and support statistical/machine-learning and econometric liquidity risk models -Support the integration and release of quant code into production systems in association with our Model Validation and Engineering partners -Day-to-day production support -Communicate modeling concepts and assumptions to external clients, product managers, sales, and risk product support unit, and engineers. This includes writing mathematical and technical documentation -Assist the QRA Team Leader with Liquidity Risk project management. This includes coordination of fellow team members as well as collaboration with -Engineering, Product Managers, and Model Validation partners -Maintain Liquidity risk methodology thought leadership. The Quant Analytics team sometimes publishes research papers in academic and industry journals

You will need to have: -Ph.D. in a quantitative field such as Mathematics, Physics, Engineering, Quantitative Finance or equivalent experience -A minimum of 4+ years on a liquidity risk modeling team (or similar team) of a buy-side or sell-side institution -Hands-on experience in designing, implementing, optimizing, and testing Machine Learning algorithms (Deep Neural Networks) and data-analysis pipelines -Demonstrable knowledge of probability theory, stochastic processes, and statistical estimation -Experience in Python and software engineering. This includes code design, implementation, testing and production release as well as working knowledge of common data science libraries -Hands-on experience in project management and delivery

We would love to see: -Strong oral and written communication skills -Collaborative mentality and enjoy working in teams with other quants, engineers, and product managers -Familiarity with object-oriented and functional design patterns, and programming -Team leadership, and ability to communicate with internal and external stakeholders and clients

If this sounds like you, please apply We will get in touch with you to let you know the next steps. In the meantime, check out #LI-DNI

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