Position: Quantitative Modeler - US Sporting Events
Location: Remote (US)
Looking for a talented and highly motivated Quantitative Modeler for a unique and exciting opportunity to work with a small team to accurately predict the outcomes of future US sporting events. Candidate must have a strong understanding the wagering markets involving these events. Candidate should have hands-on experience with statistical modeling. Ideal candidates will also have experience in traditional data science (analyzing data) and more modern data science (AI, deep learning). Candidates must be capable of working in a remote environment.
- Python (scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks)
- Experience with machine learning algorithms, such as neural networks/deep learning, SVM, XGBoost, Random Forest, generalized linear models, etc.
- Understanding of Sports Wagering Markets
- Experience with Amazon Web Services (EC2, S3, SageMaker, CodeCommit)
- Experience with MongoDB or similar NoSQL database
- Sports Analytics
- Experience with Database Management
- Experience moving research into production - serving models, hosting on cloud resources, automation, etc.
- Background in Game Theory, Control Theory
- Tensorflow or equivalent deep learning framework such asPyTorch, Caffe(2), MXNet
- Experience with ensemble methods
- 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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