Toronto, Canada
Live, first seen by us 77 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
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.
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.
Lead design and delivery of forecasting, optimization, experimentation and recommendation systems
Build production decision engines for personalization, pricing and portfolio optimization
Design multi-stage recommendation and ranking architectures
Turn ambiguous business opportunities into modeling roadmaps and KPI frameworks
Mentor Senior and mid-level Data Scientists and set modeling and experimentation standards
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.
Lead the design and delivery of high-impact decision science systems across forecasting, constrained optimization, experimentation, and batch and real-time recommendation systems
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
Translate ambiguous business opportunities into structured modeling roadmaps, milestones, and measurable KPI frameworks
Mentor Senior and mid-level Data Scientists while raising technical rigor across statistical thinking,causal inference, optimization, and experimentation
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
Strong Python proficiency across pandas, scikit-learn, PyTorch, and TensorFlow
Ability to translate long-term product vision into executable decision science roadmaps
Ability to influence DS standards, experimentation culture, and KPI rigor across the founding team
Proven experience leading ambiguous, high-impact data science initiatives from framing through production business impact
Experience as a founding or early senior hire in a new DS organization
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
Education: - Master’s degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or another related STEM field Experience:
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
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Day 77
Up longer than most. Similar jobs usually come down between day 7 and day 56.
At Sglottery, jobs usually come down by about day 44.
From 10,198 similar postings that came down, and 5 of Sglottery’s. Coming down is not always a hire.
5 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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Technical Skills: - Strong Python proficiency across pandas, scikit-learn, PyTorch, and TensorFlow - Deep expertise in statistical modeling, experimentation, causal inference, and optimization
- 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
- 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
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Yes. We’ve followed one other Staff Data Scientist job there since July 2026. It came down after 31 days.
New Data Scientist postings at the employers we follow are down 3% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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 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.
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
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.
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Toronto, Canada
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. Qualifications
No clear work-authorization passage found. Eligibility is unconfirmed.