Want to know if this job is worth applying to?
Toronto, Ontario, Canada
Hybrid
Scientific Games is the global leader in lottery games, sports betting and technology, and the partner of choice for government lotteries. From cutting-edge backend systems to exciting entertainment experiences and trailblazing retail and digital solutions, we elevate play every day. We push game designs to the next level and are pioneers in data analytics and iLottery. Built on a foundation of trusted partnerships, Scientific Games combines relentless innovation, legendary performance, and unwavering security to responsibly propel the global lottery industry ever forward.
About the Role
We are looking for a founding Senior Data Scientist to help build high-impact decision systems in a fast- paced, startup-style environment within a large organization. This is a hands-on builder role for candidates who thrive in ambiguity, move quickly from idea to production, and are energized by turning complex business problems into scalable data products.
You will work closely with Staff and Principal Data Scientists to deliver production-grade systems across forecasting, experimentation, constrained optimization, pricing, and batch and real-time recommendation systems. The role requires strong end-to-end ownership from problem framing and modeling through
production deployment using self-service ML platform tooling.
**This position will start remotely and transition to a hybrid role. Candidates must be local to Toronto, ON.
Key Responsibilities
Design, build, and deploy end-to-end decision science systems spanning demand forecasting, experimentation, portfolio optimization, pricing, and recommendation systems
Build batch and real-time recommendation pipelines using multi-stage cascading ranking architecture, including candidate generation, pre-ranking, ranking, and re-ranking
Translate ambiguous business problems into structured hypotheses, measurable KPIs, experimentation plans, and production solutions
Partner closely with MLEs to leverage self-service deployment tooling, observability, shadow deployment, canary rollout, and KPI monitoring workflows
Own one or more domain problem areas end-to-end, driving measurable business impact through fast iteration cycles
Contribute to modeling standards, code quality, validation rigor, and experimentation best practices established by Staff and Principal DS leadership
Mentor junior Data Scientists and contribute to the technical growth of the founding team
Required Qualifications
Education
Master’s degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or another related STEM field
Experience
2+ years of hands-on experience in data science, decision science, econometrics, or applied machine learning
Proven ability to independently deliver end-to-end data science systems from problem framing through measurable production impact
Demonstrated experience in at least two of: forecasting, experimentation, optimization, recommendation systems, pricing, causal inference, or portfolio science
Comfortable operating in fast-paced, startup-style environments with evolving priorities and high ownership expectations
Technical Skills
Strong Python proficiency across pandas, scikit-learn, PyTorch, and TensorFlow
Strong SQL and large-scale data manipulation experience
Solid grounding in statistical modeling, machine learning, experimentation, and optimization
Hands-on experience building production-grade batch and low-latency real-time decision systems
Familiarity with multi-stage ranking systems, ANN retrieval, embeddings, and vector search is strongly preferred
Soft Skills
Strong communication skills with ability to present complex findings to business and technical stakeholders
Collaborative mindset with ability to work cross-functionally with DS, MLE, and product teams
Strong execution bias and comfort with rapid iteration under ambiguity
Preferred Qualifications
Experience as an early or founding Data Scientist in a new team or product area
Hands-on portfolio optimization, assortment optimization, payout optimization, or mathematical programming
Experience with personalization, gaming, retail, marketplace, or digital consumer decision systems
Familiarity with Databricks, PySpark, MLflow, experimentation tooling, and cloud-native deployment workflows
Strong product intuition for balancing revenue, engagement, margin, and responsible use constraints
SG is an Equal Opportunity Employer and does not discriminate against applicants due to race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. If you’d like more information about your equal employment opportunity rights as an applicant under the law, please click here for EEOC Poster.