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About the Role
MLSE is seeking a professional to design, build, evaluate, and maintain predictive statistical and machine learning models for various aspects of player performance and acquisition. This role involves extracting insights from data, conducting research, refining models, and collaborating with engineering teams for deployment. The goal is to translate complex data into actionable insights for leadership.
Responsibilities
- Design, build, evaluate, and maintain predictive statistical and machine learning models for player evaluation, projectable skill growth, player acquisition, tactical simulation, and performance optimization.
- Extract actionable insights from spatio-temporal tracking data to drive quantitative player evaluation.
- Conduct rigorous exploratory research using advanced quantitative techniques to uncover unexploited market inefficiencies.
- Systematically audit, backtest, and refine existing internal predictive models to ensure high accuracy and adaptability.
- Partner closely with data engineering teams to design scalable features, automated data pipelines, and production workflows for seamless model deployment.
- Translate complex probabilistic outputs and model predictions into intuitive visualizations, executive briefs, and actionable insights for front-office leaders, coaches, and scouts.
Requirements
- Master’s or Ph.D. in Statistics, Data Science, Computer Science, Applied Mathematics, Operations Research, or equivalent practical quantitative research experience.
- Deep statistical learning knowledge and hands-on experience applying machine learning techniques.
- Advanced programming proficiency in Python and/or R for numerical computing, data manipulation, and model development.
- Strong command of SQL for extracting, aggregating, and joining large-scale relational datasets.
- Practical experience with software development best practices, including clean code principles, version control (Git), unit testing, and reproducible research workflows.
Skills
- Bayesian inference
- Spatial modeling
- Survival analysis
- Deep learning
- Gradient-boosted decision trees
- Hierarchical/mixed-effects models
- Neural networks
- Spatio-temporal modeling
- Python
- R
- Scikit-learn
- PyTorch
- XGBoost
- Tidyverse
- PyMC/Stan
- SQL
- Git
- Stochastic simulation methods
- Monte Carlo
- Reinforcement learning
- MLOps workflows
- Docker
- AWS
- GCP
Location
- Toronto
Work Type
- Full-time
Experience Level
- Quantitative research experience
Education Level
- Master’s or Ph.D.
Salary/Compensations
- $105,000 - $115,000
Benefits
- Email follow-up
- Candidate Portal tracking
- SMS notifications
About the Company
- Maple Leaf Sports & Entertainment Partnership (MLSE) delivers the ultimate fan experience by lifting trophies, spirits, and communities. They are a team of passionate people building the future of sport and entertainment, united as one. MLSE is the driving force behind the Toronto Maple Leafs (NHL), the Toronto Raptors (NBA), Toronto FC (MLS), Toronto Argonauts (CFL), and development teams. They operate iconic venues and offer elevated dining experiences. Through MLSE Foundation and MLSE LaunchPad, they use sport to help youth facing barriers reach their full potential, investing in Ontario communities.
Equal Opportunity
- MLSE is committed to building an equitable, diverse and inclusive organization. They are an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. MLSE will provide reasonable accommodation for qualified individuals with disabilities in the job application process.