SECURITIZED PRODUCTS - EMBEDDED QUANTITATIVE STRATEGIST - VICE PRESIDENT
Quantitative Research (QR) is an expert quantitative modeling group in J.P.Morgan, as well as a leader in financial engineering, data analytics, statistical modeling, and portfolio management.
As a global team, QR partners with a diverse set of business teams across all products and regions, contributes to sales and client interaction, product innovation, valuation, and risk management, inventory and portfolio optimization, electronic trading and market making, and appropriate financial risk controls.
As a Quantitative Strategist, you will play a crucial role in driving our business forward, leveraging QR Technology innovation and advanced analytics to address complex challenges.
Job Responsibilities : Work closely with traders and mortgage bankers, providing essential quantitative insights, including relative value analysis, to guide key decisions.
Responsible for strategizing, pricing, developing desk-specific models and solutions, and streamlining efficient automation for swift time-to-market, all to achieve measurable impacts on business outcomes.
Required Qualifications, Capabilities, and Skills : Master's degree in computer science, financial engineering, or related field;
or Bachelor's with equivalent experience) At least 3 years' experience as a Quantitative strategist or related researcher / developer role in a fast-paced transformational program, with strong stakeholder communication and management skills Development proficiency in Python, C++, and T-SQL, with demonstrated data analytics expertise, including data cleansing, normalization, and analysis or large datasets Experience building statistical regression models and pricing / analytic solutions, preferably in Fixed Income marketPreferred Qualifications, Capabilities, and Skills : In-depth understanding of SPG securitized products and mortgages, with analysis and modeling experience Experience in Commercial Real Estate (CRE) valuation and Property due diligence, or willingness to learn Familiarity with AWS Cloud environment, Machine Learning / Artificial Intelligence (ML / AI) - LLM development
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