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Canada
Remote only
This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an Applied Machine Learning Scientist based in Canada.
The Applied Machine Learning Scientist will develop and deploy advanced machine learning solutions that improve advertising performance and maximize return on investment. Working within a highly technical engineering and data science environment, you will tackle complex optimization challenges across a large-scale platform processing millions of requests every second. You will combine statistical reasoning, optimization techniques, and machine learning expertise to design innovative algorithms that drive measurable business outcomes. From exploring new approaches and prototyping models to testing them against historical data and implementing production-ready solutions, you will contribute throughout the development lifecycle. You will collaborate with engineers and data specialists to build scalable systems capable of making billions of decisions efficiently. This remote-first opportunity is ideal for a technically strong, curious scientist who enjoys solving ambiguous problems and translating research into practical, high-impact applications.
Machine Learning Algorithm Development: Design, develop, and improve machine learning algorithms that optimize advertising performance, increase campaign effectiveness, and maximize return on investment.
Research and Innovation: Explore novel algorithmic approaches, advance state-of-the-art methods, and apply established machine learning techniques to complex optimization problems.
Prototyping and Experimentation: Build prototypes of potential algorithms and data pipelines, test their performance using historical datasets, and iteratively refine solutions based on experimental results.
Production Implementation: Translate successful research and prototypes into reliable, production-ready code that can operate within large-scale, high-throughput data environments.
Cross-Functional Collaboration: Work closely with engineering and data engineering teams to integrate new algorithms into existing systems and ensure effective implementation.
Statistical Analysis and Optimization: Apply statistical methods, optimization techniques, and machine learning principles to evaluate performance, identify opportunities, and improve algorithmic outcomes.
Problem Decomposition: Take broadly defined or ambiguous technical challenges and break them down into actionable research questions, experiments, and implementation plans.
Performance Evaluation: Analyze experimental findings, identify areas for improvement, and use data-driven insights to guide algorithm development and refinement.
Scalable System Development: Contribute to solutions that support high-volume data processing, efficient decision-making, and the scalability requirements of a global advertising platform.
Continuous Technical Improvement: Stay engaged with evolving machine learning techniques and contribute ideas that strengthen the team’s research, engineering practices, and overall technical capabilities.
Education: Master’s degree or PhD in Computer Science, Statistics, Operations Research, or a related quantitative field. Additional degrees or interdisciplinary academic experience are considered an asset.
Machine Learning Expertise: Comprehensive understanding of machine learning concepts and techniques, with the ability to apply theoretical knowledge to practical, real-world challenges.
Statistics and Optimization: Strong knowledge of statistics, probability, mathematical optimization, and quantitative reasoning.
Programming Proficiency: Strong coding skills and a solid understanding of data structures and algorithms, with the ability to write efficient, maintainable production-level code.
Algorithm Development: Ability to design, prototype, test, and refine algorithms using historical data and experimental evidence.
Analytical Problem-Solving: Strong analytical skills and the ability to investigate complex problems, evaluate alternatives, and draw meaningful conclusions from data.
Independent Execution: Ability to take an ambiguous assignment, define the problem clearly, develop an actionable plan, and deliver results with appropriate autonomy.
Research-to-Production Mindset: Interest in translating machine learning research and experimentation into scalable, reliable solutions used in production environments.
Collaboration: A team-oriented approach and willingness to work closely with engineers, data scientists, and other technical colleagues in a supportive, collaborative environment.
Adaptability and Curiosity: Enthusiasm for exploring new techniques, learning continuously, and addressing challenging technical problems in a rapidly evolving field.
Remote Work Readiness: Ability to collaborate effectively in a remote-first organization and contribute to distributed technical teams.
Quantitative Academic Foundation: An undergraduate degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a closely related quantitative discipline is relevant to the role.
Competitive Compensation: Canadian base salary range of CAD $140,000–$192,500, with the final offer based on experience, technical skills, education, qualifications, location, and business needs.
Additional Compensation: Depending on eligibility and role structure, additional compensation may include annual bonuses, equity awards, or other incentives.
Remote-First Work Environment: Flexibility to work remotely, with opportunities for eligible employees to access coworking spaces.
Retirement Savings: Access to retirement, pension, or savings programs, depending on the country of employment.
Paid Time Off: Competitive paid time off, including a birthday day off.
Healthcare and Well-Being: Health benefits beginning on the first day of employment, along with access to a comprehensive mental health care program.
Work-from-Home Support: Reimbursement to help support the costs of working remotely.
Coworking Opportunities: Optional global WeWork membership for employees who prefer an alternative to working from home, with additional hubs available in selected locations.
Learning and Development: Structured onboarding and training, with support for professional development activities such as conferences, courses, and books.
Technical Education: Access to programmatic advertising courses and certifications to support continuous learning and technical growth.
Parental Leave: A parental leave program designed to support employees and their families.
Collaborative Culture: A welcoming and supportive environment that encourages teamwork, innovation, and shared learning.
Social Connection: Opportunities to participate in team activities and social events.
Inclusive Workplace: A commitment to an accessible recruitment process, workplace inclusion, and reasonable accommodations for candidates who need them.
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