Senior Data Scientist

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 Summary

About the Role

We are looking for a founding Senior Data Scientist to help build high-impact decision systems in a fast- paced, startup-style environment within a large organization. This is a hands-on builder role for candidates who thrive in ambiguity, move quickly from idea to production, and are energized by turning complex business problems into scalable data products.

You will work closely with Staff and Principal Data Scientists to deliver production-grade systems across forecasting, experimentation, constrained optimization, pricing, and batch and real-time recommendation systems. The role requires strong end-to-end ownership from problem framing and modeling through

production deployment using self-service ML platform tooling.

Qualifications

About the Role

We are looking for a founding Senior Data Scientist to help build high-impact decision systems in a fast- paced, startup-style environment within a large organization. This is a hands-on builder role for candidates who thrive in ambiguity, move quickly from idea to production, and are energized by turning complex business problems into scalable data products.

You will work closely with Staff and Principal Data Scientists to deliver production-grade systems across forecasting, experimentation, constrained optimization, pricing, and batch and real-time recommendation systems. The role requires strong end-to-end ownership from problem framing and modeling through production deployment using self-service ML platform tooling.

This role is based out of Toronto

Key Responsibilities

Design, build, and deploy end-to-end decision science systems spanning demand forecasting, experimentation, portfolio optimization, pricing, and recommendation systems

Build batch and real-time recommendation pipelines using multi-stage cascading ranking architecture, including candidate generation, pre-ranking, ranking, and re-ranking

Translate ambiguous business problems into structured hypotheses, measurable KPIs, experimentation plans, and production solutions

Partner closely with MLEs to leverage self-service deployment tooling, observability, shadow deployment, canary rollout, and KPI monitoring workflows

Own one or more domain problem areas end-to-end, driving measurable business impact through fast iteration cycles

Contribute to modeling standards, code quality, validation rigor, and experimentation best practices established by Staff and Principal DS leadership

Mentor junior Data Scientists and contribute to the technical growth of the founding team

Required Qualifications

Education

Master’s degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or another related STEM field

Experience

2+ years of hands-on experience in data science, decision science, econometrics, or applied machine learning

Proven ability to independently deliver end-to-end data science systems from problem framing through measurable production impact

Demonstrated experience in at least two of: forecasting, experimentation, optimization, recommendation systems, pricing, causal inference, or portfolio science

Comfortable operating in fast-paced, startup-style environments with evolving priorities and high ownership expectations

Technical Skills

Strong Python proficiency across pandas, scikit-learn, PyTorch, and TensorFlow

Strong SQL and large-scale data manipulation experience

Solid grounding in statistical modeling, machine learning, experimentation, and optimization

Hands-on experience building production-grade batch and low-latency real-time decision systems

Familiarity with multi-stage ranking systems, ANN retrieval, embeddings, and vector search is strongly preferred

Soft Skills

Strong communication skills with ability to present complex findings to business and technical stakeholders

Collaborative mindset with ability to work cross-functionally with DS, MLE, and product teams

Strong execution bias and comfort with rapid iteration under ambiguity

Preferred Qualifications

Experience as an early or founding Data Scientist in a new team or product area

Hands-on portfolio optimization, assortment optimization, payout optimization, or mathematical programming

Experience with personalization, gaming, retail, marketplace, or digital consumer decision systems

Familiarity with Databricks, PySpark, MLflow, experimentation tooling, and cloud-native deployment workflows

Strong product intuition for balancing revenue, engagement, margin, and responsible use constraints