Data Scientist

illumin•Toronto, ON

6d•CA$120,000 - CA$140,000•Hybrid

About The Position

Reporting to the Director, AI, we are hiring a Data Scientist to turn complex data into practical models, experiments, and recommendations that improve platform performance and business outcomes. In this role, you will apply statistical modelling, machine learning, experimentation, and applied AI techniques to solve ambiguous business problems, improve decision-making, and identify opportunities for automation and insight generation. You will work across a broad portfolio of data science initiatives, from predictive modelling and optimization to AI-powered workflows, with direct influence on how data is used across product, engineering, and business teams.

Requirements

Experience applying data science, machine learning, statistical modelling, experimentation, forecasting, recommendation systems, or optimization techniques to solve business problems.
Experience developing and refining data-driven solutions.
Proficiency in Python and SQL.
Comfort working directly with large datasets to analyze problems, test hypotheses, and develop scalable solutions.
Excellent written and verbal communication skills.
Ability to relay complex data concepts effectively to technical and non-technical stakeholders.
Ability to build trust and collaborate seamlessly with cross-functional teams, sharing knowledge and adopting constructive feedback.
Value diverse perspectives and understand how they contribute to better decision-making and innovation.
Highly analytical, innovative, and solution-oriented.
Comfortable working through ambiguity, asking thoughtful questions, and defining a path forward when challenges are not fully formed.
Resourceful and self-motivated, taking ownership of work and enjoying solving problems that require both technical expertise and business judgment.
Naturally curious and eager to learn.
Enjoy exploring new ideas, challenging assumptions, and continuously improving how problems are solved.
View setbacks as opportunities for growth, using them to refine your approach.
Stay current with emerging trends, tools, and techniques in data science, machine learning, and AI.

Nice To Haves

Experience may come from AdTech, FinTech, gaming, market research, consumer technology, IoT, digital platforms, or other data-rich environments where large volumes of data are used to drive decision-making and performance.
Ideally have exposure to building applications powered by large language models (LLMs).
Advanced education in Data Science, Statistics, Mathematics, Computer Science, Engineering, Actuarial Science, or a related discipline is considered an asset.
Experience with distributed computing, cloud-based environments, or applied AI and LLM-powered solutions is considered an asset.

Responsibilities

Design, develop, and refine algorithms that improve targeting, forecasting, ranking, recommendation, optimization, and decision-making workflows.
Build predictive models that help improve platform performance, business outcomes, and customer experiences.
Analyze large-scale datasets spanning business performance, user interactions, campaign activity, and platform operations to identify trends, uncover opportunities, and solve complex problems.
Translate ambiguous business questions into clear analytical approaches and practical recommendations.
Develop and improve recommendation systems, forecasting models, targeting strategies, and optimization frameworks.
Partner closely with Engineering to ensure data science solutions are scalable, reliable, and ready for production environments.
Design, analyze, and interpret experiments to measure the impact of new ideas, platform enhancements, and optimization strategies.
Identify opportunities to improve performance across products, workflows, and business outcomes using statistical analysis and data-driven decision-making.
Support the development of AI and LLM-powered solutions that improve workflows, automate processes, and generate meaningful insights.
Evaluate emerging technologies and contribute to initiatives that bring practical AI capabilities into the business.
Partner with Product, Engineering, Sales, Finance, and leadership teams to understand challenges, develop solutions, and communicate findings.
Translate technical concepts into clear recommendations that help stakeholders make informed decisions and drive measurable impact.