- New York, NY, USA
- Permanent, Full time
- Credit Suisse -
- 25 Apr 18
Data Architect for QT Fund # 104178
The Fund uses quantitative methodologies to deliver consistent, low volatility, positive return stream with limited drawdowns. These methodologies are built upon a robust, reliable and accurate data. The Fund is seeking an authoritative data architect to develop next generation data platforms to satisfy the teams large and growing data needs.
- Lead role in the design of The Fund's next generation data architecture to handle large and growing data sets.
- 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 precise 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 analytic 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-KC1*
- 5+ years of experience working with large scale data in production/research environments.
- Creative 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.
- Doctoral degree in Computer Science, Statistics or related discipline.
- Understanding of economic and financial concepts; experience in quantitative investment environment.
- Practical experience with data processing, database programming (e.g. SQL) and data analytics.
- Experience with Big Data storage and processing (Hadoop, etc.).