BettingJobs is thrilled to collaborate with our client, a prominent online bookmaker known for transforming the sports betting and gaming industry, in their search for a Machine Learning Engineer to join their team based in Toronto.
Key Responsibilities
- Develop, maintain, and improve predictive models for sports analytics and betting applications in live production environments.
- Build tools for model evaluation, performance analysis, and data visualization to support decision-making.
- Monitor and refine model performance based on betting market trends, gameplay dynamics, data quality, and rule changes.
- Apply statistical and machine learning techniques to solve complex sports modelling problems.
- Work with large datasets and write efficient, scalable, and maintainable code.
- Create dashboards and visualizations using tools such as Shiny, ggplot2, and Plotly.
- Collaborate with traders, analysts, and engineers to deliver high-quality predictive solutions.
- Contribute to code reviews, testing, deployment processes, and development best practices.
Requirements
- Degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Strong experience with statistical computing in R, including packages such as data.table, dplyr, and tidyr.
- Experience developing model evaluation and visualization tools using ggplot2, Shiny, Plotly, or similar frameworks.
- Solid understanding of statistical and machine learning techniques, including regression models, GLMs, and GAMs.
- Experience working with large datasets and relational databases such as PostgreSQL, SQL Server, or BigQuery.
- Understanding of software engineering principles, version control (Git), and collaborative development practices.
- Strong analytical, problem-solving, and communication skills.
- Passion for sports, sports analytics, sports betting, or fantasy sports.
Preferred Qualifications
- Experience with probabilistic graphical models, Bayesian techniques, Stan, or NIMBLE.
- Familiarity with Rcpp, C++, profiling, optimization, or parallel computing.
- Experience deploying models into production environments.
- Exposure to sports betting, financial markets, or similar data-driven industries.
