Data Scientist III

TD•Toronto, ON

1d•CA$96,900 - CA$136,800•Onsite

About The Position

This role sits within the Wealth AI2 team, supporting Test & Learn projects. We are looking for an experienced Analytics Engagement Lead to lead the design, execution, and delivery of end-to-end Test & Learn initiatives. This role combines strategic consulting, advanced analytics, experimentation methodology, and stakeholder management to help business partners make data-driven decisions. The successful candidate will be a hands-on analytics leader who can independently design experiments, perform statistical analyses, communicate insights to senior executives, and mentor junior analysts while partnering closely with business stakeholders across the organization to influence decision-making.

Requirements

Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, Data Science, or a related quantitative discipline.
5 years of experience in analytics, experimentation, consulting, or data science.
Demonstrated experience leading end-to-end Test & Learn or experimentation initiatives.
Experience working directly with business stakeholders and senior leadership.
Advanced proficiency in SQL.
Strong programming skills in Python.
Experience working with PySpark and large-scale datasets.
Solid understanding of statistical inference, hypothesis testing, confidence intervals, regression analysis, sampling methodologies, and experimental design.
Experience with A/B testing, causal inference, uplift measurement, or related experimentation methodologies.
Familiarity with data visualization and presentation tools.
Excellent executive presentation and storytelling skills.
Strong consulting and stakeholder management capabilities.
Ability to simplify complex analytical concepts for non-technical audiences.
Strong problem-solving skills with a bias toward delivering measurable business outcomes.
Experience mentoring or reviewing the work of junior analysts.

Responsibilities

Lead the end-to-end delivery of Test & Learn initiatives, from problem framing and experimental design to analysis, recommendations, and business adoption.
Partner with business stakeholders to identify opportunities where experimentation can drive measurable business value.
Design robust A/B tests, randomized controlled trials, quasi-experiments, and other experimental approaches appropriate for business scenarios.
Define success metrics, sample size requirements, test duration, and measurement methodology.
Analyze experiment results and translate statistical findings into actionable business recommendations.
Perform hands-on data extraction, transformation, analysis, and modeling using SQL, Python, and PySpark.
Build scalable analytical workflows and reusable assets to improve experimentation efficiency.
Ensure data quality, analytical rigor, and reproducibility throughout the analysis lifecycle.
Apply statistical techniques to evaluate causal impact, quantify uncertainty, and validate business outcomes.
Provide technical leadership and analytical guidance to junior analysts.
Conduct peer reviews of code and analytical solutions to ensure accuracy, efficiency, and adherence to development standards.
Coach team members on experimentation best practices, coding standards, and analytical storytelling.
Promote best practices in Test & Learn methodologies across the analytics team.
Act as a strategic thought leader to business stakeholders, build strong partnerships with business leaders and cross-functional teams, confidently challenge assumptions, shape problem definitions, and influence decisions to balance business needs with analytical rigor and feasibility.
Translate complex analytical concepts into clear, business-friendly recommendations.
Present insights, recommendations, and experiment results to senior executives with confidence and clarity.
Influence business decisions through evidence-based storytelling and data-driven recommendations.