Applied Machine Learning Scientist

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. 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.