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.