Applied Machine Learning Scientist

Accountabilities: 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. Requirements 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. Benefits 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.