Data Scientist

Role Overview
As a Data Scientist, Product Analytics at McAfee, you will lead advanced analytics and experimentation to guide product strategy and improve customer experience. You will own the end-to-end analytics process — from defining metrics and designing experiments to building predictive models — to uncover actionable insights that shape how millions of users interact with McAfee's security products.

This role requires both strategic thinking and hands-on technical execution, with opportunities to influence product direction, mentor team members, and drive data excellence across the organization.

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.

About The Role

• Lead data-driven experimentation: Design, execute, and analyze A/B tests to evaluate product and feature impact on engagement, retention, and customer satisfaction.
• Drive product insights: Conduct deep-dive analyses on user journeys, feature adoption, and product funnels to identify opportunities for growth and optimization.
• Develop predictive models: Apply machine learning and statistical techniques to forecast behavior, personalize user experience, and reduce churn.
• Elevate analytics quality: Partner with engineering and data platform teams to ensure high-quality telemetry data, reliable pipelines, and consistent business definitions.
• Collaborate cross-functionally: Work closely with Product, UX, and Engineering to translate insights into actionable product improvements.
• Mentor and guide junior data scientists and analysts, promoting best practices in experimentation design, causal inference, and data storytelling.
• Communicate insights effectively to product and executive stakeholders through clear, visual narratives that drive decision-making.

About You

• 5–8 years of industry experience in data science, experimentation, or product analytics.
• Proven track record of applying causal inference methods such as difference-in-differences, matching, regression discontinuity, or instrumental variables.
• Strong experience with A/B testing design, implementation, and interpretation.
• Proficient in Python (pandas, scikit-learn, stats models) and SQL; experience with data visualization tools (Power BI, Tableau, or Looker) preferred.
• Solid understanding of product telemetry data and experience analyzing user engagement, adoption, or retention patterns.
• Experience developing and deploying machine learning models in product or business contexts.
• Excellent problem-solving, communication, and collaboration skills — comfortable influencing decisions at all levels.
• Bachelor's or master's degree in a quantitative field (Statistics, Computer Science, Engineering, Mathematics, or related) is preferred.

Company Overview
McAfee is a leader in personal security for consumers. Focused on protecting people, not just devices, McAfee consumer solutions adapt to users' needs in an always online world, empowering them to live securely through integrated, intuitive solutions that protects their families and communities with the right security at the right moment.

Company Benefits And Perks
We work hard to embrace diversity and inclusion and encourage everyone at McAfee to bring their authentic selves to work every day. We're proud to be Great Place to Work Certified in 10 countries, a reflection of the supportive, empowering environment we've built where people feel seen, valued, and energized to reach their full potential and thrive.

We offer a variety of social programs, flexible work hours and family-friendly benefits to all of our employees.

• Bonus Program
• Pension and Retirement Plans
• Medical, Dental and Vision Coverage
• Paid Time Off
• Paid Parental Leave
• Support for Community Involvement

We're serious about our commitment to diversity which is why McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.