We are aggressively expanding our cutting-edge data science team as we seek to grow the SimpleBet team. You will help design and build machine learning algorithms that will be used by sports betting institutions to maximize profits and efficiently manage risk. To do well in this role you need first-rate problem solving skills, an interest in the sports or betting markets, as well as experience with statistics, machine learning, data structures and algorithms, and computer science fundamentals.
- Apply statistical techniques to analyze data, and engineer relevant signals for sports markets pricing
- Implement baseline machine learning-based pricing models, and improve upon them iteratively
- Learn and implement cutting-edge machine learning algorithms to improve pricing accuracy
- Write production worthy code to productize any algorithms designed, ensuring they run in an online real-time fashion
- Apply statistics to and develop algorithms in problem spaces beyond pricing markets, such as user tailoring, determining betting limits, and managing risk
- Collaborate with management to align algorithm development with customer needs, business needs, and data science/risk engine vision
- Provide recommendations on algorithms and problem spaces to explore as you work with and analyze SimpleBet data
- Write hardware-conscious, parallelized code to help productize predictive models
- Code efficient data stores and structures to store information at all levels of the memory hierarchy
- Collate data from a multitude of sources into efficient systems
Apply for this job
- Bachelor’s degree or Master’s degree or PhD from an accredited university or college in statistics, mathematics, or related quantitative field.
- Experience with low-level programming languages like C or Rust
- Experience with graph algorithms and graph databases is a major plus
- Strong interest in sports, the sports markets, and/or the betting markets
- Experience in sports analytics is preferred.
- Strong understanding of programming and computer science fundamentals
- Strong knowledge of statistics, AI/machine learning algorithms, and pattern recognition techniques.
- Experience in productizing algorithms, and improving their efficiency/runtime
- Understanding of Python, SQL, R, or related data processing languages.
- Strong computer science, math, and statistics knowledge