Risk-Bengaluru-Vice President-Operational Risk Analytics

The Risk division is responsible for credit, market and operational risk, model risk, independent liquidity risk, and insurance throughout the firm. 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 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. 5+ years of financial or non-financial risk industry experience. ABOUT GOLDMAN SACHS 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 /careers. We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process.