Quantitative Researcher (Data Science Group)
Apply NowCompany: Squarepoint Capital
Location: New York, NY 10025
Description:
Squarepoint Services US LLC seeks a full-time Quantitative Researcher for its New York, NY location.
On behalf of an investment management organization, conduct extensive research on various machine-learning algorithms and their applications to extract value from data. Evaluate the effectiveness of different models and techniques; stay up to date with advancements and innovations in the field of machine learning and its application in finance, keep abreast of academic literature and industry reports. Analyze large datasets using advanced statistical methods and the time series-oriented programming language KDB Q. Collaborate with fellow quantitative researchers and trading teams to identify trading opportunities. Develop predictive models based on historical market data using machine-learning frameworks like XGboost and PyTorch and use these models to generate trade signals. Orchestrate workflows of computations to train these models at scale on Slurm high-performance computing grids using the workflow management tool Snakemake, and optimize the training on GPU (Graphics Processing Units). Work closely with software engineers and developers to build custom tools and libraries for processing financial data with most advanced machine learning algorithms. Create visualization tools and dashboards to help fellow quantitative researchers and trading teams understand key metrics and performance indicators. Prepare and deliver presentations on research findings to senior management, communicate technical concepts and insights clearly and effectively, participate in team meetings and discussions to share ideas and collaborate on projects. Oversee the deployment and monitoring of tools used in daily trading processes. Ensure that all tools are functioning properly and provide feedback to the technology teams.
Requirements: Minimum of Master's degree, or foreign equivalent, in any Science, Technology, Engineering, and/or Mathematics (STEM) field of study and at least two (2) years of experience as a Quantitative Researcher, or related occupation for a financial services/investment management/investment banking organization. Must have at least two (2) years of employment experience with each of the following required skills: (1) Working with the optimization of machine learning models, decision trees and neural networks using machine learning frameworks (XGBoost, PyTorch), data processing and feature; (2) Building predictive models to generate trade signals for various assets traded on financial markets, and various trading opportunities; (3) Utilizing statistical analysis of large datasets, implementation of optimization algorithms, communication of research results and key performance metrics to senior management, fellow quantitative researchers and trading teams; (4) Orchestrating large scale distributed machine learning workflows on high performance computing grids (Slurm) using Snakemake workflow management tool; (5) Out-of-core distributed training of machine learning models, optimization of training on GPUs; (6) Implementing machine learning models in Python using the data science and machine learning libraries (Numpy, Pandas, Sciki-Learn, PyTorch, XGBoost); (7) Utilizing manipulation of Timeseries data stored in KDB Q; (8) Working with Low-level optimizations of core libraries to train Machine Learning models (XGBoost, PyTorch)
Salary / Rate Minimum/yr: : $150,000
Salary / Rate Maximum/yr : $170,000
The minimum and maximum salary/rate information above include only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.
Squarepoint is an EEO/AA employer.
On behalf of an investment management organization, conduct extensive research on various machine-learning algorithms and their applications to extract value from data. Evaluate the effectiveness of different models and techniques; stay up to date with advancements and innovations in the field of machine learning and its application in finance, keep abreast of academic literature and industry reports. Analyze large datasets using advanced statistical methods and the time series-oriented programming language KDB Q. Collaborate with fellow quantitative researchers and trading teams to identify trading opportunities. Develop predictive models based on historical market data using machine-learning frameworks like XGboost and PyTorch and use these models to generate trade signals. Orchestrate workflows of computations to train these models at scale on Slurm high-performance computing grids using the workflow management tool Snakemake, and optimize the training on GPU (Graphics Processing Units). Work closely with software engineers and developers to build custom tools and libraries for processing financial data with most advanced machine learning algorithms. Create visualization tools and dashboards to help fellow quantitative researchers and trading teams understand key metrics and performance indicators. Prepare and deliver presentations on research findings to senior management, communicate technical concepts and insights clearly and effectively, participate in team meetings and discussions to share ideas and collaborate on projects. Oversee the deployment and monitoring of tools used in daily trading processes. Ensure that all tools are functioning properly and provide feedback to the technology teams.
Requirements: Minimum of Master's degree, or foreign equivalent, in any Science, Technology, Engineering, and/or Mathematics (STEM) field of study and at least two (2) years of experience as a Quantitative Researcher, or related occupation for a financial services/investment management/investment banking organization. Must have at least two (2) years of employment experience with each of the following required skills: (1) Working with the optimization of machine learning models, decision trees and neural networks using machine learning frameworks (XGBoost, PyTorch), data processing and feature; (2) Building predictive models to generate trade signals for various assets traded on financial markets, and various trading opportunities; (3) Utilizing statistical analysis of large datasets, implementation of optimization algorithms, communication of research results and key performance metrics to senior management, fellow quantitative researchers and trading teams; (4) Orchestrating large scale distributed machine learning workflows on high performance computing grids (Slurm) using Snakemake workflow management tool; (5) Out-of-core distributed training of machine learning models, optimization of training on GPUs; (6) Implementing machine learning models in Python using the data science and machine learning libraries (Numpy, Pandas, Sciki-Learn, PyTorch, XGBoost); (7) Utilizing manipulation of Timeseries data stored in KDB Q; (8) Working with Low-level optimizations of core libraries to train Machine Learning models (XGBoost, PyTorch)
Salary / Rate Minimum/yr: : $150,000
Salary / Rate Maximum/yr : $170,000
The minimum and maximum salary/rate information above include only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.
Squarepoint is an EEO/AA employer.