Stas Brown

Stas Brown

Liquidity Risk Analytics Lead Examiner @ Reserve

About Stas Brown

Stas Brown serves as the Liquidity Risk Analytics Lead Examiner at the Federal Reserve Bank of New York, where he has worked since 2019. He has a background in financial systems analysis and technology support, with experience at the Federal Reserve Board and Marist College.

Current Role at Federal Reserve Bank of New York

Stas Brown serves as the Liquidity Risk Analytics Lead Examiner at the Federal Reserve Bank of New York. He has held this position since 2019. In this role, he focuses on liquidity risk management and collaborates on projects addressing industry-wide liquidity topics. His work involves analyzing risk management practices, assessing policy implications, and contributing to discussions on systemic financial stability.

Previous Experience at Federal Reserve Board

Before his current role, Stas Brown worked as a Financial Systems Analyst at the Federal Reserve Board from 2016 to 2019. During his tenure in Washington, DC, he contributed to various financial systems projects and gained valuable experience in liquidity risk analysis.

Background in Technology and Education

Stas Brown has a background in technology, having worked as a Senior Desktop Technician at Marist College from 2012 to 2015 and as a Technology Department Assistant at Landon School from 2011 to 2014. He studied at Marist College, where he earned a Bachelor of Business Administration with an Emphasis in International Business from 2012 to 2015.

Contributions to Liquidity Risk Management

In his current role, Stas Brown represents the Federal Reserve in a liquidity workgroup with international regulators, providing insights on domestic liquidity issues. He synthesizes financial metrics from Federal Reserve System data collections and collaborates on the identification and presentation of emerging risks.

Technical Skills and Tools

Stas Brown has developed and implemented enhancements to the Liquidity Program's analytical tools. He utilizes SQL, Tableau, and Python in his work, which supports the analysis of liquidity risk and the overall effectiveness of risk management practices.

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