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
Eligible candidates are preferred to have the following:
Masters or Bachelors degree in a quantitative discipline such as data science, mathematics, physics, econometrics, computer science or engineering. Entrepreneurial, analytically creative, self-motivated and team-oriented. Excellent written, verbal and team-oriented communication skills. Experience with programming for extract transform load (ETL) operations and data analysis (including performance optimization) using languages such as, but not limited to, Python, Java, C++, SQL and R. 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. Working knowledge of the financial industry, markets and products and associated non-financial risk. Working knowledge of mathematics including statistics, time series analysis and numerical algorithms. 3+ years of financial or non-financial risk industry experience..
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