Senior Data Scientist I - Meals

Accountabilities:: Lead data science for the Meals initiative, supporting the post-MVP scale-up of data science systems, measurement, and infrastructure. Design, build, and maintain metric-tracking frameworks to measure engagement, activation, reactivation, and broader product impact. Define and lead experimentation strategies, from hypothesis development and experiment design through analysis, interpretation, and recommendations. Apply A/B testing, causal inference, statistical modeling, and product analytics to guide product strategy and decision-making. Define meaningful KPIs and optimize product funnels to support product-market fit and sustainable growth. Partner closely with Product Managers, Engineering Leads, Data Engineers, and Designers to align data science with product strategy. Communicate complex technical findings and analytical insights clearly to senior business and engineering stakeholders. Improve logging, instrumentation, metric definitions, and measurement practices across complex data environments. Connect offline evaluation metrics with online experimentation and business outcomes where relevant. Contribute to a high-visibility 0→1 initiative, helping establish what success looks like as the product moves toward scale. Requirements: 4+ years of professional experience in data science or a related quantitative field. Proven experience in consumer-facing product data science and close collaboration with Product, Engineering, and Data teams. Strong foundation in product and data analysis, A/B experimentation, causal inference, and statistical modeling. Demonstrated ability to define KPIs, analyze product funnels, and use data to support product-market fit. Excellent communication skills, with the ability to synthesize complex analytical and technical findings for senior stakeholders. Bachelor’s degree in Statistics, Computer Science, Mathematics, Economics, Engineering, or another quantitative discipline, or equivalent practical experience. Experience with causal inference techniques beyond standard A/B testing, such as holdout groups, difference-in-differences, or quasi-experimental methods, is preferred. Experience connecting offline evaluation metrics such as NDCG, precision/recall, or human evaluation with online experiments and business outcomes is preferred. Proven experience improving logging, instrumentation, and metric definitions in complex data environments is a plus. Experience launching new features in new markets or contributing to 0→1 initiatives is preferred. Strong analytical thinking, structured problem-solving, stakeholder management, and cross-functional collaboration skills. Ability to operate effectively in a high-visibility environment where experimentation and data-driven decision-making are central to product development. Remote eligibility is currently limited to Ontario, Alberta, British Columbia, and Nova Scotia. Benefits: $161,000–$170,000 CAD annual base salary for eligible Canadian-based candidates. New-hire equity grant. Annual equity refresh grants. Market-competitive compensation and benefits based on permanent work location. Fully remote work within eligible Canadian provinces. Flexible approach to where work is performed, supported by opportunities for regular in-person connection and team events. Opportunity to work on a high-visibility 0→1 product initiative with significant data science ownership. Close collaboration with Product, Engineering, Design, and Data teams. Exposure to experimentation, causal inference, product analytics, and large-scale measurement systems.