Product Analyst

McAfee•Waterloo, ON

17d•Hybrid

About The Position

As a Product Analyst at McAfee, you will drive data-informed product decisions by delivering actionable insights, designing robust experiments, and deeply understanding user behavior. You will partner closely with Product, UX, and Engineering to shape product strategy and improve customer experience across McAfee’s security products. This is a Hybrid Position located in either Waterloo or Toronto, Canada. We are only considering candidates within a commutable distance to either location. You will be required to be onsite on an as-needed basis; when not working onsite, you will work from your home office.

Requirements

5+ years of experience in product analytics, advanced analytics, or data science with strong product focus.
Strong experimentation expertise: Hands-on experience designing and interpreting A/B tests and quasi-experimental methods (e.g., difference-in-differences, matching).
Advanced analytical skills: Proficient in SQL and Python for data analysis; strong foundation in statistics and hypothesis testing.
Product analytics experience: Deep understanding of user behavior analysis, funnel optimization, retention, and feature adoption metrics.
Data visualization & storytelling: Experience with BI tools such as Power BI, Tableau, or Looker to communicate insights effectively.
Telemetry & data modeling knowledge: Experience working with event-based data and defining tracking for digital products.
Business and product acumen: Ability to connect data insights into product strategy and customer experience improvements.
Strong communication and stakeholder management skills, with the ability to influence decisions across teams.
Bachelor’s or master’s degree in a quantitative field a plus (Statistics, Computer Science, Engineering, Mathematics, or related).

Responsibilities

Lead experimentation strategy: Design, execute, and analyze A/B tests and quasi-experiments to evaluate product and feature impact on engagement, retention, and customer satisfaction.
Drive product insights: Conduct deep-dive analyses on user journeys, onboarding funnels, feature adoption, and retention cohorts to identify growth and optimization opportunities.
Define and operationalize metrics: Establish north-star metrics, KPIs, and guardrails; ensure consistent definitions across teams and dashboards.
Enable product decision-making: Translate complex analyses into clear, actionable recommendations for product roadmaps and prioritization.
Improve data foundations: Partner with data engineering and platform teams to ensure high-quality telemetry, scalable data models, and reliable reporting layers.
Leverage advanced analytics: Apply statistical techniques (segmentation, cohort analysis, regression, causal inference) to uncover drivers of user behavior and product performance.
Collaborate cross-functionally: Work closely with Product Managers, Designers, and Engineers to embed analytics into the product development lifecycle.
Mentor and elevate analytics practices: Guide analysts on experimentation design, metric definition, and storytelling best practices.
Communicate effectively: Deliver clear, compelling insights to stakeholders and leadership through dashboards, presentations, and narratives.