Sobre este puesto de Data Scientist en Maple Leaf Sports & Entertainment
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.
We're 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, we’re 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, we’ve invested more than $45 million into Ontario communities -- and we’re 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 you're ready to play with purpose, grow with passion, and win as one, we’d 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 we’re 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
- 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, 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:
$105,000 - $115,000
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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 [email protected]. This email is only for accommodation requests. Resumes sent to this email address will not be considered.
