Staff Data Scientist
- Compensation
- Not disclosed
- Location
- Toronto, Canada
- Work setup
- Hybrid
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Data science
- Posted
Skills
Must have
- 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
- Master’s degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or another related STEM field
Nice to have
- 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
Full description
Scientific Games:
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 Role
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. Despite being part of a large organization, the team operates with a startup mindset: fast-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 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