Background 

Analytics & Reporting (A&R) is a group within Risk Engineering in the Risk Division of Goldman Sachs. The group ensures the firm’s senior leadership, investors and regulators have a complete view of the positional, market, and client activity drivers of the firm’s risk profile allowing them to take actionable and timely risk management decisions. 

Risk Engineering is a multidisciplinary group of quantitative experts who are the authoritative producers of independent risk & capital metrics for the firm. Risk Engineering is responsible for modeling, producing, reviewing, interpreting, explaining and communicating risk & capital metrics and analytics used to ensure the firm adheres to its Risk Appetite and maintains the appropriate amount of Risk Capital. Risk Engineering provides risk & capital metrics, analytics and insights to the Chief Risk Officer, senior management, regulators, and other firm stakeholders. 

Role Responsibilities 

A&R delivers critical regulatory and risk metrics & analytics across risk domains (market, credit, liquidity, operational, capital) and firm activities via regular reporting, customized risk analysis, systematically generated risk reporting and risk tools​.  A&R has a unique vantage point in the firm’s risk data flows that, when coupled with a deep understanding of client and market activities, allows it to build scalable workflows, processes and procedures to deliver actionable risk insights​. The following are core responsibilities for A&R:  Delivering regular and reliable risk metrics, analytics & insights based on deep understanding of the firm’s businesses and its client activities.  Building robust, systematic & efficient workflows, processes and procedures around the production of risk analytics​ for financial & non-financial risk, risk capital and regulatory reporting.  Attesting to the quality, timeliness and completeness of the underlying data used to produce these analytics​. 

Qualifications, Skills & Aptitude  

Masters or Bachelors degree in a quantitative discipline such as data science, mathematics, physics, econometrics, computer science or engineering. 1-3 years of experience, preferably in financial, regulatory or consulting environment Working knowledge of mathematics including statistics, time series analysis and numerical algorithms.  Working knowledge of the financial industry, markets and products and associated non-financial risk. Entrepreneurial, analytically creative, self-motivated and team-oriented.  Excellent written, verbal and team-oriented communication skills.  Experience with programming in Python and SQL for extract transform load (ETL) operations and data analysis (including performance optimization). Experience in using languages such as R, Java, C++ is beneficial.  Experience in developing data visualization and business intelligence solutions using tools such as, but not limited to, Tableau, Alteryx, PowerBI, and front-end technologies and languages. 

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