Asset & Wealth Management-Salt Lake City-Vice President, Quantitative Engineering–10412228

About the role

Lead the development, implementation, and documentation of scenarios comprised of a broad range of economic and financial variables for businesses within the Firm. Collaborate with internal stakeholders, analyzing user needs from a scenario design perspective and addressing data, model, and implementation issues.

Analyze large data sets (structured and unstructured) to build predictive models of business-relevant market variables. Develop, refine, and improve scenarios by leveraging knowledge in financial markets, economics, current events, statistical analysis, and programming.

Build and challenge risk models, identify and quantify vulnerabilities across market, credit, liquidity risk and modeling. Create and maintain clear and complete technical documentation of the risk-model performance testing approach and process. Mentor junior and mid-level team members.

Requirements

Master’s degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Computational Finance, Applied Mathematics, or related quantitative field and three (3) years of experience in the job offered or a related quantitative engineering role.

Alternatively, a Bachelor’s degree (U.S. or foreign equivalent) in the same fields and five (5) years of experience in the job offered or a related quantitative engineering role.

Alternatively, a PhD degree (U.S. or foreign equivalent) in the same fields and one (1) year of experience in the job offered or a related quantitative engineering role.

Skills

Prior experience must include three (3) years (with a Master’s degree), five (5) years (with a Bachelor’s degree), or one (1) year (with a PhD degree) with at least five of the following eight skills:

• C++, Java, or Python;
• Performing financial mathematics, including at least one of the following: stochastic calculus, no-arbitrage pricing theory, multivariable calculus, linear algebra, probability theory, numerical methods, or Monte-Carlo techniques;
• Performing analysis leveraging market risk, credit risk, liquidity risk, or mathematical finance concepts;
• Object-oriented programming and scripting programming languages such as Python or Java;
• Implementing mathematical models or analytics in production-quality software;
• Working with database query languages, such as SQL, MongoDB, or other data management tools to process large datasets;
• Applying algorithms or data structures to write complex programs;
• Developing pricing models for financial products to model risk, economics, and cash flows under normal and distressed market environments.