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Quantitative Researcher – Equity Mid-Frequency Factor
| Company | Squarepoint Capital |
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| Location | New York, NY, USA |
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| Salary | $250000 – $270000 |
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| Type | Full-Time |
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| Degrees | Master’s |
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| Experience Level | Mid Level |
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Requirements
- Must have a minimum of a Master’s degree or foreign equivalent in any STEM (Science, Technology, Engineering, or Math) field of study
- 2 years of experience as a Quantitative Researcher, Data Research Analyst, or related position for an investment/asset management organization
- At least two (2) years of employment experience with generating economic insights including earnings forecasts utilizing alternative datasets and programming languages including R, SQL and Python
- Using machine learning techniques to perform clustering analysis on data and extracting features and signals from datasets
- Creating machine learning models to evaluate investment hypothesis and utilizing natural language processing techniques to clean and preprocess complex textual data
- Using cloud computing, Linux, and Shell to automate tasks – data ingestion, data outlier detection, feature generation, and daily execution of return forecast processes
- Python programming with PyData packages, Pandas, Numpy and Scipy to perform data analysis
- Developing applications with KDB, Python and Shell to monitor data and feature quality related to return forecasts and live trading
- Result forecasting and investment research for public companies
- Big Data Analysis and high-performance computing with BigQuery
Responsibilities
- Formulate mathematical and simulation models of investment strategies
- Enhance trading through computerized algorithms and implementation of models
- Utilize comprehensive knowledge of mathematical models and technologies, statistical techniques including regression analysis, machine learning, and statistical inference
- Produce and implement sophisticated analyses describing new statistical effects
- Assess robustness of effects and develop new quantitative strategies
- Perform validation and testing of both trading simulations and critical trading applications
- Build applications utilizing Shell and Python to automate daily data dependency processing for trading strategies
- Utilize KDB/Q and Python to analyze existing strategy behavior and propose and implement improvements
- Utilize Excel/VBA mathematical models and KDB analysis tools to track market history of specific asset classes
- Manage live trading automatons and perform continuous monitoring of risk related to live trading automatons
- Leverage on asset-class-specific experience to find new patterns in market data
- Explore new methods to optimize execution costs
- Utilize extensive knowledge of market structure and statistical arbitrage to improve on existing trading strategies
- Assist team’s senior quantitative researcher’s efforts in building, validating, releasing, and maintaining highly complex automated trading models
- Pilot research projects spanning multiple teams across multiple regions to develop new mathematical models and analytical tools for critical investment decision making
Preferred Qualifications
No preferred qualifications provided.
