Founding Staff Data Scientist: Decision Science Leader

sglottery; Toronto; Hybrid; CAD 120,000 - 180,000; Full time

Job summary

Scientific Games in Toronto is seeking a founding Staff Data Scientist to build the decision science function from the ground up and drive high‑value modeling across forecasting, optimization, and personalized recommendations.

Starting remotely with a transition to a hybrid setup, you will partner with senior scientists to set standards, establish KPI frameworks, and mentor growing DS talent while delivering measurable business impact.

Qualifications

• Master's degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or another related STEM field.
• 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.
• Experience with self-service experimentation and ML platforms (e.g., Databricks, PySpark, MLflow).

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

Skills

Python

Pandas

Scikit-learn

PyTorch

TensorFlow

SQL

Statistical Modeling

Causal Inference

Forecasting

Optimization

Experimentation

Recommender Systems

Mentorship

Education

Master's or PhD in STEM field

Tools

Databricks

PySpark

MLflow

Job description

Scientific Games in Toronto is seeking a founding Staff Data Scientist to build the decision science function from the ground up and drive high‑value modeling across forecasting, optimization, and personalized recommendations.

Starting remotely with a transition to a hybrid setup, you will partner with senior scientists to set standards, establish KPI frameworks, and mentor growing DS talent while delivering measurable business impact.