Posted 9 months ago
Applied Scientist, Sales AI
@ Amazon Development Centre Canada ULC
Toronto, Ontario
Overview
Amazon Development Centre Canada ULC is hiring an Applied Scientist for its Sales AI team in Toronto, a full-time role. The work involves building machine learning and generative AI models that generate actionable advertising insights and recommendations for account teams. The posting states a base salary of 149,300 to 249,300 CAD annually.
Responsibilities:
- Conduct research and build machine learning and generative AI models for advertising sales
- Run A/B experiments and statistical analysis
- Partner with engineers and product teams to deploy models into production
- Translate findings into recommendations for stakeholders and senior management
Requirements summary:
- 3+ years building business models
- PhD, or Master's with 4+ years of relevant experience
- Publications or patents in top peer-reviewed venues
- Programming in Python, Java or C++
Tools & skills
Benefits mentioned
Source and dates
Source type not verified · amazon
- Employer posting date
- 2026-02-23
- First seen by our source
- 2026-10-10
- Source record updated
- 2026-11-06
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Mid level |
| Department | Data & analytics |
| Experience | 3+ years |
| Hours per week | 40 (estimated) |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
| Education | Master’s degree (required), Doctorate (required) |
| Fields of study | Computer science, Computer engineering, Machine learning, Related field |
| Perks mentioned | Generous paid time off, Retirement plan, Performance bonus, Stock or equity |
Summary and details written by AI from the posting; the employer’s posting is the authority.
Amazon Development Centre Canada ULC
| Field | Value |
|---|---|
| Open jobs on Jobsy | 163 |
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 focused on creating customer-facing recommendations related to our advertising products and budget allocations, 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. We are truly working on the cutting edge of innovation within Deep Learning, RL, Generative AI and Sequential Recommender Systems within the advertising and sales domains.
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
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
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