Data Scientist
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Job Summary
Powered by Passion. United by Purpose. Build for Impact.
At Maple Leaf Sports & Entertainment Partnership (MLSE) we exist to deliver the ultimate fan experience by lifting trophies spirits and communities united as one.
Were more than a workplace. We are a team of passionate people boldly building the future of sport and entertainment together. We believe in the power of play the strength of collaboration and the energy that comes from showing up with purpose.
From the ice to the pitch the hardwood to the digital arena were proud to be the driving force behind the Toronto Maple Leafs (NHL) the Toronto Raptors (NBA) Toronto FC (MLS) Toronto Argonauts (CFL) and development teams with the Toronto Marlies (AHL) Raptors 905 (NBA G League) Toronto FC II (MLS NEXT Pro League) and Raptors Uprising Gaming Club the Toronto Raptors Esports franchise in the NBA 2K League.
We bring these teams and world-class entertainment to life at our iconic venues including Scotiabank Arena BMO Field Coca-Cola Coliseum Ford Performance Centre BMO Training Ground and OVO Athletic Centre. Off the field we serve up elevated dining at e11even Real Sports and our signature club spaces like Hot Stove Club ScotiaClub and Platinum Club.
Through MLSE Foundation and MLSE LaunchPad we use the power of sport to help youth facing barriers reach their full potential. Since 2009 weve invested more than $45 million into Ontario communities -- and were just getting started.
This is what it means to be One MLSE: a culture where everyone plays a role everyone belongs and everyone contributes to something bigger than themselves.
So if youre ready to play with purpose grow with passion and win as one wed love to have you on our team.
** We know that great candidates come from a variety of backgrounds and experiences. Even if you do not meet every qualification listed we encourage you to apply. Your unique perspective transferable skills and lived experience may be exactly what were looking for. **
Job Description
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 (e.g. Bayesian inference spatial modeling survival analysis or deep learning) to uncover unexploited market inefficiencies.
- Systematically audit backtest and refine existing internal predictive models to ensure high accuracy and adaptability through changes in game rules or industry economics.
- 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.
Qualifications
- Masters 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 such as gradient-boosted decision trees hierarchical/mixed-effects models neural networks or spatio-temporal modeling.
- Advanced programming proficiency in Python and/or R for numerical computing data manipulation and model development (using libraries such as scikit-learn PyTorch XGBoost tidyverse or PyMC/Stan).
- 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.
Nice-to-haves:
- Experience with stochastic simulation methods (e.g. Monte Carlo) or reinforcement learning for game strategy optimization and decision modeling under uncertainty.
- A portfolio of public sports analytics research open-source sports data science projects or competition entries evaluating athlete performance or tactical dynamics.
- Familiarity with modern MLOps workflows containerization (Docker) and cloud infrastructure (AWS or GCP) for scaling predictive models.
Job Posting Compensation Range/Rate:
$105000 - $115000
Were glad youre here. Once you apply well follow up by email and you can track everything in your Candidate Portal. Want instant updates Opt-in for SMS notificationsto stay in the loop.
At MLSE we are committed to building an equitable diverse and inclusive organization. We are an equal opportunity employer and we 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. If you have difficulty using our online application system and you need an accommodation due to a disability please email . This email is only for accommodation requests. Resumes sent to this email address will not be considered.
Required Experience:
IC
