Corporate Treasury, Liquidity Meterics Strats, Quantitative Engineering, Dallas, Vice President

Goldman Sachs•Dallas, TX

25d•Onsite

About The Position

Liquidity and Prime Risk Strats use their engineering and mathematical background to identify and measure risk and to implement quantitative and technical risk modelling solutions. Successful Strats are highly analytical, driven to own commercial outcomes, and communicate with precision and clarity. As a part of the team, you will work with our key business partners and understand financial markets to quantify the firm’s liquidity risk. You will also focus on developing quantitative models & scalable architecture.

Requirements

At least 7 years of prior experience in the financial industry, preferably in Capital Markets, Risk or Treasury functions
Strong quantitative skills with an advanced degree in Mathematics, Physics, Engineering or other highly quantitative discipline
Strong programming experience in at least one compiled or scripting language (e.g. C, C++, Java, Python)
Strong written and verbal communication skills – ability to explain complex quantitative concepts to a non-technical audience
Strong analytical and problem solving skills using math, statistics, and programming
Demonstrated ability to learn technologies and apply

Nice To Haves

Familiarity with financial markets, financial assets and liquidity risk management practices is a plus

Responsibilities

Develop, implement, and maintain quantitative measures of liquidity risk using advanced mathematical/statistical/engineering approaches
Perform quantitative analysis and facilitate understanding of a variety of financial instruments, including secured funding transactions, collateral firm and client inventory, and loans and commitments
Quantify and monitor measures of risk in different areas across the firm, such as prime brokerage, synthetic trading, and repo trading
Work alongside revenue generating functions and corporate treasury to implement the liquidity regulatory requirements
Communicate clearly complex mathematical concepts with internal and external stakeholders such as risk managers, senior management and regulators.
Updating and maintaining risk models along with business growth and risk environment changes
Developing and maintaining large scale risk infrastructures/systems in a compiled or scripting language