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Market Data Scientist fo QT Fund Ltd, Asset Management
Credit Suisse -
January 6, 2018
New York, New York
We Offer Market Data Scientist for QT Fund Ltd, Asset Management
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.
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.
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.).
Excellent pattern recognition and predictive modelling skills.