Applied Machine Learning Scientist
New
J
JobgetherAdvertising technology
Based in CanadaFull-Time
SalaryCanadian base salary range of CAD $140,000–$192,500
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Job Details
- Required Skills
- Machine Learning
Requirements
- Master’s degree or PhD in Computer Science, Statistics, Operations Research, or a related quantitative field.
- Relevant undergraduate degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a closely related quantitative discipline.
- Comprehensive understanding of machine learning concepts and techniques, with the ability to apply them to practical challenges.
- Strong knowledge of statistics, probability, mathematical optimization, and quantitative reasoning.
- Strong coding skills and understanding of data structures and algorithms.
- Ability to write efficient, maintainable, production-level code.
- Ability to design, prototype, test, and refine algorithms using historical data and experimental evidence.
- Ability to investigate complex problems, evaluate alternatives, and draw conclusions from data.
- Ability to define ambiguous problems, develop actionable plans, and deliver results autonomously.
- Interest in translating machine learning research and experimentation into scalable production solutions.
- Ability to collaborate with engineers, data scientists, and other technical colleagues in a remote-first organization.
Responsibilities
- Design, develop, and improve machine learning algorithms to optimize advertising performance and campaign effectiveness.
- Explore algorithmic approaches and apply machine learning methods to complex optimization problems.
- Build algorithm and data-pipeline prototypes, test them on historical datasets, and refine them based on experimental results.
- Translate successful research and prototypes into reliable, production-ready code.
- Collaborate with engineering and data engineering teams to integrate algorithms into existing systems.
- Apply statistical methods, optimization techniques, and machine learning to evaluate and improve algorithmic outcomes.
- Break ambiguous technical challenges into research questions, experiments, and implementation plans.
- Analyze experimental findings and use data-driven insights to guide algorithm development.
- Contribute to scalable solutions for high-volume data processing and efficient decision-making.
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