Market Data Scientist fo QT Fund Ltd, Asset Management

We Offer
Market Data Scientist for QT Fund Ltd, Asset Management

Function Overview

The Fund's investment objective is to deliver a consistent, low volatility, positive return stream with limited drawdowns. The Investment Manager seeks to achieve this objective by developing and running a variety of quantitative, systematic trading and investment strategies. Specifically, the Investment Manager's personnel formulate hypotheses about the drivers of asset returns and apply a rigorous scientific approach to design, develop, implement and manage strategies around these hypotheses.

We Offer:
  • Lead for data set development from raw data into trading/research solution, including understanding of how to extract insights from data using statistical techniques.
  • Work with IT developers on seamless integration of new data with trading and research systems, both on internal systems and in cloud.
  • Meet with data vendors and research alternative and traditional data sets.
  • Manage evaluation strategy for new data sets and organize trial periods, including improving existing frameworks.
  • Responsible for cataloguing and keeping track of data sets and vendors, including rules to ensure data set integrity.
  • Design data statistics package to evaluate internal data set usage and availability.

Credit Suisse maintains a Working Flexibility Policy, subject to the terms as set forth in the Credit Suisse United States Employment Handbook.

*LI-CSJOB*

You Offer
  • Innovative thinker who is passionate about using technology in different ways.
  • You are comfortable working under pressure and possess analytical skills and troubleshooting abilities.
  • Works both independently and within a team, including facing various internal teams.
  • You have good communication skills and are comfortable being external facing/relationship owner.
  • Advanced degree in Applied Math, Computer Science, Statistics or related field.
  • Deep understanding of economic and financial concepts, especially as applied to a quantitative environment.
  • Practical experience with ETL, data processing, database programming (SQL, MarkLogic, Mongo etc) and data analytics.
  • Experience with Big Data storage and searches (Hadoop, etc.).
  • You are proficient with scripting languages (Python, R, JavaScript) and object oriented languages (C#, C++), and comfortable working in a Linux environment.
  • Excellent pattern recognition and predictive modelling skills.