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A startup: Quantitative Modeler [Remote, US]


A Startup is seeking a talented and highly motivated Quantitative Modeler for a unique and exciting opportunity with a small team looking to accurately predict the events (team and player level) of future sports competitions.



At: a startup
Location: Remote (US)
Position: Quantitative Modeler

This position has been filled.

Looking for a talented and highly motivated Quantitative Modeler for a unique and exciting opportunity with a small team looking to accurately predict the events (team and player level) of future sports competitions. Candidate should have hands-on experience in traditional data science (analyzing data) and more modern data science (AI, deep learning). Degree in a relevant scientific field required (mathematics, statistics, data science, actuarial sciences, etc.). Candidates should have a high level of enthusiasm toward sports analytics and prediction of future outcomes in the sports world. Candidates should be capable of working in a remote environment.

Required Qualifications: 

  • Python (scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks)
  • Tensorflow or equivalent deep learning framework such as PyTorch, Caffe(2), MXNet
  • Experience with machine learning algorithms, such as neural networks/deep learning, SVM, Random Forest, generalized linear models, etc.
  • Experience with Financial Market Analysis (Trading systems, investment analysis, Hedging, high frequency trading, etc.)

Desired Qualifications: 

  • R, MATLAB, SAS, mongoDB, SQL Server 
  • Sports Analytics
  • Experience with Database Management
  • Background in Game Theory, Control Theory

Candidates will be considered if any of the following apply:

  • BS degree with 3+ years of experience in analytics
  • MS graduates with focus in analytics
  • Outstanding experience in sports analytics

Job Description

  • Investigate, identify, develop and optimize new methods, algorithms and technologies to derive novel, competitive insights from disparate data sources.
  • Collaborate with other data scientists and developers to identify, design, build and maintain tools, analytical workflows and applications to streamline and strengthen current processes.
  • Applying new and emerging analytical methods and visualization technologies on real world data for the purposes of building investment strategies around the outcomes of sporting events.

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