Staff Data Scientist in Toronto

Scientific Games is the global leader in lottery games, sports betting and technology, and the partner of choice for government lotteries. We are looking for a founding Staff Data Scientist to help build the decision science function from the ground up and translate our long-term product and decisioning vision into scalable production systems. This is not a maintenance role.

As an early senior technical leader, you will work closely with the Principal Data Scientist, Staff peers, and Senior Data Scientists to define the modeling standards, decision science patterns, and execution playbooks that will become the backbone of the organization. This role sits at the intersection of technical depth, platform leverage, and strategic execution.

You will own some of the organization’s highest-value problems across:

• Forecasting
• Experimentation
• Personalization
• Recommendation systems
• Portfolio optimization
• Pricing
• Player decision systems

This position will start remotely and transition to a hybrid role. Candidates must be local to Toronto, ON.

Key Responsibilities

• Lead the design and delivery of high-impact decision science systems across forecasting, constrained optimization, experimentation, and batch and real-time recommendation systems.
• Translate ambiguous business opportunities into structured modeling roadmaps, milestones, and measurable KPI frameworks.
• Partner with the Principal Data Scientist to establish modeling standards, experimentation guardrails, validation frameworks, and deployment playbooks for the founding DS organization.
• Build production-grade decision engines spanning player personalization, next-best-action systems, pricing, portfolio optimization, and retail recommendation use cases.
• Drive the design of multi-stage recommendation and ranking architectures, including retrieval, pre-ranking, ranking, and re-ranking.
• Mentor Senior and mid-level Data Scientists while raising technical rigor across statistical thinking, causal inference, optimization, and experimentation.
• Shape the evolution of reusable DS workflows that integrate cleanly with the self-service ML platform being built by the founding MLE team.

Required Qualifications

• Education: Master's degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or another related STEM field.
• Experience: 6+ years post-Master's experience or 4+ years post-PhD experience in data science, decision science, econometrics, or applied machine learning.
• Proven experience leading ambiguous, high-impact data science initiatives from framing through production business impact.
• Strong experience in at least three of: forecasting, optimization, experimentation, recommendation systems, pricing, portfolio science, or causal inference.
• Experience mentoring Data Scientists and shaping technical standards beyond individual project delivery.
• Technical Skills: Strong Python proficiency across pandas, scikit-learn, PyTorch, and TensorFlow.
• Deep expertise in statistical modeling, experimentation, causal inference, and optimization.
• Strong SQL and large-scale data experience.
• Hands-on experience building batch and real-time recommendation or decision systems.
• Familiarity with multi-stage cascading ranking architectures and decision APIs.

Leadership

• Ability to translate long-term product vision into executable decision science roadmaps.
• Strong technical mentorship and review discipline.
• Ability to influence DS standards, experimentation culture, and KPI rigor across the founding team.

Preferred Qualifications

• Experience as a founding or early senior hire in a new DS organization.
• Hands-on portfolio optimization, payout optimization, assortment optimization, or mathematical programming.
• Experience with personalization, gaming, retail, marketplace, or digital consumer decision systems.
• Experience working with self-service experimentation and ML platforms.
• Familiarity with Databricks, PySpark, MLflow, and cloud-native deployment workflows.
• Strong product intuition for balancing revenue, margin, player engagement, and responsible gaming constraints.