This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Data Scientist based in Canada.
This is a senior, hands-on data science role focused on turning revenue opportunities into production machine learning systems. You’ll work across personalization, experimentation, subscriptions, transactions, advertising, and other consumer revenue experiences. The role combines deep statistical and causal analysis with the practical delivery of scalable ML models and services. You’ll partner closely with Product, Engineering, Data Engineering, and MLOps teams to bring models into user-facing experiences. As part of an AI-native environment, you’ll use modern generative AI tools to accelerate discovery, modeling, experimentation, and development. You’ll also help shape technical standards, mentor other data scientists, and influence the roadmap for production ML. Your work will directly support products used by more than 100 million monthly active users while helping responsible monetization decisions stay grounded in evidence.
- Investigate revenue opportunities and underlying data, translating findings into opportunities that can be sized, prioritized, tested, and measured.
- Design, build, deploy, and operate production ML systems, including batch inference, online services, and online learning models for personalization, experimentation, and automation.
- Partner with Product, Mobile Engineering, Cloud Engineering, Data Engineering, and MLOps teams to integrate ML capabilities into customer-facing experiences.
- Establish monitoring and measurement frameworks to evaluate model performance, product outcomes, and business impact.
- Implement data, code, and model lineage practices that support secure, reproducible, and compliant ML lifecycles.
- Collaborate with Data Engineering to improve pipelines and the broader data ecosystem supporting experimentation and machine learning.
- Apply causal inference, experimentation, and advanced analytics to guide product and revenue decisions.
- Mentor data scientists and contribute to best practices for advanced analytics, experimentation, and production ML development.
- Use Claude Code and other AI tools throughout data discovery, modeling, coding, experiment evaluation, and workflow optimization.
- Share effective AI workflows and help raise the technical and AI fluency of surrounding data science and engineering teams.
Requirements
- 6+ years of professional experience scoping, building, analyzing, and deploying ML-powered systems, including models successfully shipped to production.
- Advanced degree in a quantitatively rigorous discipline involving sophisticated statistical analysis, or equivalent industry experience.
- Strong programming experience with Python, scikit-learn, PySpark, and tools for causal inference, alongside solid software engineering practices such as testing, modularization, and version control.
- Demonstrated technical training and professional experience applying modern causal inference and causal analysis techniques.
- Strong experience working with existing datasets, designing instrumentation to generate new data, and building transformations for complex analyses and ML systems.
- Hands-on experience designing, monitoring, and analyzing experiments in consumer technology environments.
- Experience building ML systems or running experiments within B2C technology, ideally involving subscriptions, transactions, advertising, or other revenue-generating products.
- Strong communication, stakeholder management, and project leadership skills, with the ability to influence cross-functional teams and explain complex analytical findings clearly.
- A structured, hypothesis-driven approach to solving ambiguous problems, including situations where data is incomplete or unavailable.
- Experience leveraging LLMs and generative AI for advanced data processing, analysis, development, or other data science workflows.
- Experience with digital advertising, ad technology infrastructure, content ranking, or feed algorithms is an advantage.
- Familiarity with production feature stores, ML model registries, subscription products, lifecycle marketing, user acquisition, revenue forecasting, or geospatial and mobile location data is preferred.
- Demonstrated ability to critically review AI-generated code, analyses, and models and take full ownership of production outcomes.
- Willingness to continuously explore emerging AI tools, run practical experiments with them, and translate useful discoveries into team practices.
Benefits
- Competitive annual base salary for Canada of $171,500–$202,500 CAD, with final compensation determined by experience, skills, background, and location.
- Equity compensation through Restricted Stock Units (RSUs) as part of the total compensation package.
- Comprehensive benefits including medical, dental, vision, life, and disability coverage; supplemental medical and dental benefits are available for Canadian employees.
- RRSP with a DPSP plan for Canadian employees.
- Paid parental leave.
- Mental Wellness Program and Employee Assistance Program (EAP).
- Flexible paid time off, companywide holidays, and summer and winter shutdown periods.
- Learning and development programs to support continued professional growth.
- Equipment, tools, and reimbursement support for an effective remote working environment.
- Remote-first work culture for employees working from Canada.
- Complimentary premium membership and connected-device benefits.
