Applied Scientist, Sales AI

Amazon; Toronto; On-site; CAD 149,000 - 249,000; Full time

Benefits offered by this job

Health benefits

RRSP

DPSP

Paid time off

Job summary

Amazon Development Centre Canada ULC in Toronto is seeking an Applied Scientist to advance AI/ML models for Advertising Sales. You will lead research, design scalable models, and work with engineers and product teams to translate findings into business value.

The role emphasizes experimental methodology, publication quality, and collaboration across disciplines. Strong programming skills and a PhD or MS with extensive experience are preferred in a fast-paced, AI-driven environment.

Qualifications

• PhD or Master’s with 4+ years in CS/CE/ML
• 3+ years building models for business applications
• Experience publishing in top-tier conferences or journals
• Experience programming in Java, C++, Python

Responsibilities

• Conceptualize and lead state-of-the-art ML/AI research to optimize all aspects of the Ad Sales business
• Lead the design and implementation of successful models and algorithms with cross-functional teams
• Run regular A/B experiments, gather data, and perform statistical analysis
• Improve scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving
• Publish scientific findings in reports and papers
• Partner with software engineering and product management to define success metrics and drive adoption of new features
• Lead requirements gathering sessions with product teams and business stakeholders
• Maintain scientific documentation for product initiatives
• Translate complex scientific findings into actionable business recommendations for stakeholders
• Provide clear reports and presentations on models and services
• Communicate results to internal teams to share best practices

Skills

Java

C++

Python

Algorithms & data structures

Numerical optimization

Data mining

Parallel computing

High-performance computing

Education

PhD or Master’s + 4+ years CS/CE/ML

Job description

Description

Are you interested in shaping the future of Advertising and B2B Sales? We are a growing team with an exciting AI-first charter and need your passion, innovative thinking, and creativity to help take our products to new heights.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We break fresh ground in product and technical innovations every day! Within the Advertising Sales organization, we are building a central AI/ML team and are seeking top Applied Science talent to help us build new, science-backed services that drive success for our customers. Our goal is to transform the way account teams operate by creating actionable insights and recommendations they can share with their advertising accounts, and ingesting Generative AI throughout their end-to-end workflows to improve their work efficiency.

As an Applied Scientist on the team, you will bring deep expertise in quantitative modeling techniques such as Sequential Recommender Systems, Deep Learning, Reinforcement Learning or Hidden Markov Models. You have the scientific and technical skills to build and refine models that can be implemented in production, and you leverage Natural Language Processing and Generative AI models to enhance their explainability. You will contribute to chart new courses with our ad sales support technologies, and you have the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers. You will be part of a team of fellow scientists and engineers taking on iterative approaches to tackle big, long-term problems. You are fluently able to leverage the latest Generative AI systems and services to accelerate and improve your work while maintaining high quality in your work outputs.

Key job responsibilities

Scientific Modeling

• Conceptualize and lead state-of-the-art research on new Machine Learning and Generative Artificial Intelligence solutions to optimize all aspects of the Ad Sales business
• Lead the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects
• Run regular A/B experiments, gather data, and perform statistical analysis
• Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving
• Publish scientific findings in reports and papers that can be shared internally and externally

Product Development Support

• Partner with software engineering and product management teams to support product and service development, define success metrics and measurement approaches, and help drive adoption of innovative new features for our services.
• Lead requirements gathering sessions with product teams and business stakeholders
• Maintain scientific documentation and knowledge for product initiatives

Collaboration & Communication

• Work closely with software engineers to deliver end-to-end solutions into production
• Translate complex scientific findings into actionable business recommendations for stakeholders and senior management
• Provide clear, compelling reports and presentations on a regular basis with respect to your models and services
• Communicate with internal teams to showcase results and identify best practices.

About The Team

Sales AI is a central science and engineering organization within Amazon Advertising Sales that powers selling motions and account team workflows via state-of-the-art of AI/ML services. Sales AI is investing in a range of sales intelligence models, including the development of advertiser insights, recommendations and Generative AI-powered applications throughout account team workflows.

Basic Qualifications

• 3+ years of building models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

• Experience using Unix/Linux
• Experience in professional software development
• Deep understanding of modeling frameworks that support sequential decision making such as Hidden Markov Models, Sequential Recommender Systems and Reinforcement Learning

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being.

We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.

CAN, ON, Toronto - 149,300.00 - 249,300.00 CAD annually

Company

Amazon Development Centre Canada ULC