Liquidity Data Science

Liquidity Data Science

Job Title: Liquidity Data Science
Contract Type: Permanent
Location: Bangalore Urban
Reference: 6_220318
Contact Name: Pooja Mungekar
Contact Email:
Job Published: March 22, 2018 19:16

Job Description

Our client, one of the well known Investment Banks is looking for a Data Scientist with minimum 4 years of experience for their office in Bangalore. 

 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

1. Writing models to detect anomalies in structured datasets

2. Writing algorithms to learn the signals and interactions between different data streams and resulting liquidity and liquidity risks.

3. 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