VP, Quantitative Engineering — Risk Modeling & Analytics

Goldman Sachs; Salt Lake City (UT); On-site; USD 180,000 - 260,000; Full time

Job summary

Goldman Sachs is seeking a Vice President, Quantitative Engineering in Salt Lake City to lead the development, implementation, and documentation of scenarios across a broad range of economic and financial variables for the Firm. You will collaborate with internal stakeholders, analyze user needs from a scenario design perspective, and address data, model, and implementation issues.

The role involves analyzing large datasets to build predictive models of market variables, refining scenarios with

Qualifications

• Master’s degree or PhD required with 3–5 years of related experience or 1 year with PhD in quantitative roles.
• Experience with C++, Java, or Python and financial mathematics concepts preferred.
• Experience with data processing using SQL or similar data management tools.

Responsibilities

• Lead development, implementation, and documentation of complex scenarios across economic and financial variables.
• Collaborate with internal stakeholders to analyze user needs from a scenario design perspective and resolve data/model/implementation issues.
• Analyze large datasets to build predictive market-variable models and refine scenarios using financial markets and statistics knowledge.
• Mentor junior and mid-level team members and contribute to technical documentation of risk-model testing.

Skills

C++

Java

Python

Financial modeling

Monte Carlo methods

SQL

Education

Master’s degree in Mathematics/CS/FE

Bachelor’s degree in Mathematics/CS/FE

PhD in related quantitative field

Job description

Goldman Sachs is seeking a Vice President, Quantitative Engineering in Salt Lake City to lead the development, implementation, and documentation of scenarios across a broad range of economic and financial variables for the Firm. You will collaborate with internal stakeholders, analyze user needs from a scenario design perspective, and address data, model, and implementation issues.

The role involves analyzing large datasets to build predictive models of market variables, refining scenarios with