Risk - Market Risk & Capital Analysis - Quantitative Risk Supervision - Analyst/Associate - Bengaluru

  • Competitive
  • Bangalore, Karnataka, India
  • Permanent, Full time
  • Goldman Sachs International
  • 18 Feb 18 2018-02-18

Risk - Market Risk & Capital Analysis - Quantitative Risk Supervision - Analyst/Associate - Bengaluru

The Risk group is responsible for credit, market and operational risk, model risk, independent liquidity risk, and insurance throughout the firm.

The mission of Risk Division is to effectively identify, monitor, evaluate and manage the firm's financial and operational risks in support of the firm's strategic plan. Through comprehensive processes, which include critical analysis, evaluating stress scenarios, dynamically managing risk, and prudently balancing risk and reward, the Risk Department plays a critical second line of defense role.

Market Risk Management & Analysis ("MRMA"), has primary responsibility for independently assessing, monitoring, and managing market risk at the firm.

Within MRMA, the Market Risk and Capital Analysis group ("MRCA") is responsible for the independent supervision of risk appetite through the reporting, analysis and escalation of market risk exposures, limit setting and enforcement. To execute this, MRCA leverages data and information from a variety of sources including those independently calculated by MRMA. MRCA risk managers also provide a qualitative overlay and challenge to the quantitative aspects of the risk management process. In addition, MRCA manages significant relationships with internal and external stakeholders such as senior managers, risk committees, boards and regulators. Quantitative Risk Supervision ("QRS") is a new team with MRCA whose mission is to enhance the effectiveness of risk management by using statistical, mathematical and computational techniques to create systematic empirical approaches to risk supervision.

The global scope of risk engineering requires the processing of large volume datasets and intensive calculations to support the global view of firm capital and liquidity risks.

Problem Space
The firm ecosystem of trades, prices, and business activity data provides a rich opportunity set to apply big data and machine learning techniques to optimize one of the world's most complex and sophisticated financial institutions. Feature detection algorithms are required to extract the complex interaction between trading activity and the resulting market risk impacts as the banks systems efficiently identify risks and quantify vulnerabilities.

QRS Role
The QRS team is responsible for driving projects to automate and innovate the market risk management process in partnership with the existing functions within the Risk Division.

There are four different broad conceptual strands to the work of the QRS team:
In order to create an effective function that can achieve risk supervision objectives by using systematic empirical approaches, certain environmental factors have to be in place regarding Data Access, Data Completeness and Data Organisation. QRS will actively engage with building the foundational blocks that are needed in order to create the right environment.
Analysis generates information from data. The QRS team will develop and run computer software that will extract information relevant for risk management from the firm's data set. The aim is to both automate and innovate the process of obtaining insight into the firm's risk profile by systematically classifying the data features that correspond to relevant risk information.
Supervisory Frameworks:
An example of a supervisory framework is a limit framework comparing data observations to thresholds and then generates actions if thresholds are exceeded. The QRS team will improve the quality of existing supervisory frameworks through using data to design and back test the performance of those frameworks. The QRS team will also be able to show the effectiveness of supervisor frameworks through analysis of data.
Supervisory Action:
A Supervisory Action is an intervention designed to achieve a Supervisory Outcome that is triggered by the occurrence of a Supervisory Event. By defining Supervisory Events through data features, they can be detected by an algorithm. In addition, a set of Supervisory Actions that would achieve the required Supervisory Outcome could also be generated algorithmically in many cases. This means that substantial parts of the supervision process could be automated by the QRS team.

The QRS team provides a unique opportunity to create data driven solutions to guide critical decisions at senior levels in the firm. Team members are immersed in risk management process where they are empowered to create novel tools, analytics, and data visualizations to transform the market risk function.

The QRS team has been created to nucleate a critical mass of expertise to leverage the application of sophisticated data analytics and machine learning techniques. Team members are expected to possess the expertise to apply techniques such as regressions, support vector machines, naive Bayes classifications, and clustering analysis with rigor and creativity.

Key skill sets:

  • The role requires an advanced degree (Masters/ PhD strongly preferred) in a computational field (Computer Science, Applied Mathematics, Engineering, or related quantitative disciplines)
  • Basic applied statistics, machine learning techniques (supervised / unsupervised techniques)
  • Strong data structures and algorithms
  • Strong programming skills in at least one programming language (R/ python / java / c++)
  • Strong problem and analytical solving
  • Strong work ethic, self-driven, ownership, collaborative

The Goldman Sachs Group, Inc. is a leading global investment banking, securities and investment management firm that provides a wide range of financial services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals. Founded in 1869, the firm is headquartered in New York and maintains offices in all major financial centers around the world.

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