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Senior Data Scientist

Goldman Sachs
5 to 10 years
5 to 10 years
68% Matching
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  About opportunity

Team: Liquidity Data Sciences Overview Finance & Risk is responsible for reporting P&L, monitoring and managing Balance Sheet, Liquidity Risk, Market Risk, Credit Risk and Operational Risk. Finance and Risk engineering creates solutions to monitor and manage P&L, balance sheet, liquidity risk and capital risk metrics which are used to manage the firm:

  • Measure the profitability and capital consumption of businesses.
  • Determine the incremental cost of capital and liquidity risk of a transaction.
  • Size the liquidity required to support firm activity during crisis periods or to satisfy regulatory requirements.
  • Determine counterparty exposure.

The global scope of finance and 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.
  • The volume of data requires anomaly detection algorithms to automatically identify erroneous price or trade information.
  • Other machines are required to extract the complex interaction between trade structures and the resulting liquidity impacts as the banks systems efficiently fund risks and obfuscate the direct drivers.
  • A close partnership between the senior firm liquidity and capital management and the liquidity data science team amplifies the impact of this group.

Liquidity Data Scientist Role
The Liquidity Data Sciences team is responsible to drive projects to automate the flow of validated high-quality data and to build algorithms to leverage the firm’s global data sets to optimize liquidity management in partnership with Corporate Treasury by

  • Writing models to detect anomalies in structured datasets
  • Writing algorithms to learn the signals and interactions between different data streams and resulting liquidity and liquidity risks.
  • Employ models to forward project liquidity and analytics which guide the firm as it seeks to optimize liquidity risk management and liquidity actions.

The Liquidity Data Science 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 the business groups which execute daily liquidity risk management where they are empowered to innovate novel tools, analytics, and data visualizations to transform the corporate treasury functions.

The Liquidity Data Science team has been created to nucleate a critical mass of expertise to leverage the application of sophisticate data analytics and machine learning techniques. Team members are expected to possess the expertise to apply techniques such as regressions, support vector machines, naïve 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
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Senior Data Scientist

Goldman Sachs   •   Bangalore