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.Position SummaryAbout the RoleWe 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. Despite being part of a large organization, the team operates with a startup mindset: rapid‐paced, highly iterative, and biased toward rapid execution, learning, and measurable business impact. You will own some of the organization's highest‐value problems across forecasting, experimentation, personalization, recommendation systems, portfolio optimization, pricing, and player decision systems.**This position will start remotely and transition to a hybrid role. Candidates must be local to Toronto,



ON.QualificationsKey ResponsibilitiesLead the design and delivery of high-impact decision science systems across forecasting, constrained optimization, experimentation, and batch and real-time recommendation systemsTranslate ambiguous business opportunities into structured modeling roadmaps, milestones, and measurable KPI frameworksPartner with the Principal Data Scientist to establish modeling standards, experimentation guardrails, validation frameworks, and deployment playbooks for the founding DS organizationBuild production‐grade decision engines spanning player personalization, next‐best‐action systems, pricing, portfolio optimization, and retail recommendation use casesDrive the design of multi‐stage recommendation and ranking architectures, including retrieval, pre‐ranking, ranking, and re‐rankingMentor Senior and mid‐level Data Scientists while raising technical rigor across statistical thinking,causal inference, optimization, and experimentationShape the evolution of reusable DS workflows that integrate cleanly with the self‐service ML platform being built by the founding MLE teamRequired QualificationsEducation:Master's degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or another related STEM fieldExperience:6+ years post-Master's experience or 4+ years post-PhD experience in data science, decision science, econometrics, or applied machine learningProven experience leading ambiguous,



high-impact data science initiatives from framing through production business impactStrong experience in at least three of: forecasting, optimization, experimentation, recommendation systems, pricing, portfolio science, or causal inferenceExperience mentoring Data Scientists and shaping technical standards beyond individual project deliveryTechnical Skills:Strong Python proficiency across pandas, scikit-learn, PyTorch, and TensorFlowDeep expertise in statistical modeling, experimentation, causal inference, and optimizationStrong SQL and large-scale data experienceHands‐on experience building batch and real‐time recommendation or decision systemsFamiliarity with multi‐stage cascading ranking architectures and decision APIsLeadership:Ability to translate long‐term product vision into executable decision science roadmapsStrong technical mentorship and review disciplineAbility to influence DS standards, experimentation culture, and KPI rigor across the founding teamPreferred Qualifications:Experience as a founding or early senior hire in a new DS organizationHands‐on portfolio optimization, payout optimization, assortment optimization, or mathematical programmingExperiencewithpersonalizationgamingretailmarketplaceordigitalconsumerdecisionsystemsExperience working with self-service experimentation and ML platformsFamiliarity with Databricks, PySpark, MLflow, and cloud‐native deployment workflowsStrong product intuition for balancing revenue, margin, player engagement, and responsible gaming constraintsSG 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 #J-18808-Ljbffr

📌 Staff Data Scientist (Toronto)
🏢 Sglottery
📍 Toronto