Staff Data Analyst I (P5) (SQL/Python)

Location: Hybrid in Canada, with 2 days per week in the office in Toronto or Montreal.

Company

Build analytics capabilities that improve understanding of users and support product, commercial, revenue, cost, retention, and growth decisions.

What you will do

• Set shared standards for analytical methods, definitions, quality assurance, reusable code, and analytical rigor; review colleagues' work.
• Build reusable dashboards, visualizations, and automated alerts to identify metric changes and data-quality issues early.
• Lead deep user and behavioral analysis and create new user segments for tracking, experimentation, and product personalization.
• Connect user insights and analysis to revenue, cost, retention, growth, and other financial or business outcomes.
• Partner with the experimentation owner to improve experimentation standards and develop new methods and decision models.
• Mentor analysts and keep the team current with industry techniques.

Requirements

• Advanced SQL for production-grade analytics and dashboard layers.
• Python for statistical analysis, automation, and reusable libraries.
• Track record of raising analytical quality and visualization or monitoring standards across a team, beyond delivering individual work.
• Strong user and behavioral analysis experience, including building user segments, and the ability to connect analytics to financial and business outcomes such as revenue, retention, and growth.
• Solid experimentation fundamentals, including frequentist and Bayesian approaches, power, and guardrails, plus a collaborative approach to shared standards.
• Ability to turn complex analytical work into clear, decision-grade narratives for senior stakeholders.
• Experience mentoring and teaching others.

Nice to have

• Machine learning experience with propensity or uplift modeling, forecasting, or causal methods.
• Analytics-grade data modeling.

Culture & Benefits

• Hybrid work model in Canada with 2 days per week in the Toronto or Montreal office.
• Opportunity to shape analytical quality, dashboard clarity, monitoring, and user understanding across the analytics team.
• Collaborative environment focused on shared standards, mentoring, knowledge transfer, and continuous learning.