Corporate Treasury - Quantitative Engineering - Analyst - London

Corporate Treasury lies at the heart of Goldman Sachs, ensuring all the businesses have the appropriate level of funding to conduct their activities, while also optimizing the firm's liquidity and managing its risk.

The Funds Transfer Pricing (FTP) Strats team within Treasury is dedicated to developing robust quantitative Asset Liability Management (ALM) models and frameworks. This enables the accurate pricing, incentivization, and execution of funding transfers, utilizing liabilities such as deposits and notes, to support the Firm's asset-generating revenue activities. The team collaborates closely with Treasury traders, deposit and revenue businesses, Risk, and senior leadership across the Firm to strategically manage the overall balance sheet and enhance risk management.

• 
  
• Incentivize the desks' decision making to align with Firm's overarching financial objectives while operating within established risk and regulatory frameworks
  
• Efficiently execute funds transfer through the utilization of internal funding instruments developed and managed by the FTP strats team
  
• Manage deposits pricing models to accurately quantify and assess interest rate risks across all deposits in the Firm
  
• Develop and manage effective quantitative models that facilitate the transfer of market risks in Treasury, enabling centralized managed by the Treasury trading desk
  

• 
  
• Design and implement quantitative frameworks and mathematical models to accurately price funding for Firm's assets and liabilities
  
• Develop fixed-income tradable models for internal funding transfer and risk centralization as well as pricing models for deposits based to quantify deposits risk to markets sensitivities
  
• Understand business needs, data requirements and specifications; facilitate and develop process workflow required to support implementation of data engineering solutions
  
• Develop analytics and reporting to provide transparency on FTP and ALM
  
• Analyze model output and facilitate understanding of model results by non-technical clients
  
• Manage/Execute end-to-end systems development cycle from requirements analysis, coding, testing, UAT and post go live maintenance
  

• 
  
• Advanced degrees (PhD or Masters) in a quantitative field such as Mathematics, Physics, Engineering, or Computer Science -- or bachelor with relevant work experience
  
• Strong analytical and problem-solving ability
  
• Python or similar programming language
  
• Excellent communication skills, including experience speaking to both technical and business audiences and working globally across multiple regions
  
• Familiarity with financial markets, financial products, and optimization is a plus
  
• Self-motivated team player
  

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.

We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

The Goldman Sachs Group, Inc., 2023. All rights reserved.

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.