Careers

The Inventory Placement team within the Supply Chain Optimization Technologies organization seeks a Principal Data Scientist to lead the design and implementation of experiments and causal impact analyses across all placement initiatives. Key job responsibilities The Principal Data Scientist in Inventory Placement will seek to measure all initiatives, improvements, and initiatives in inventory placement to help guide development towards the most impactful and cost-effective ideas. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from massive datasets. You will work closely with engineers, business intelligence engineers and technical product managers to obtain relevant datasets and create experimental designs, statistical models, and review key results with business leaders and stakeholders. Your work will exhibit a balance between high scientific standards and business practicality. To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and deliver results that meet high scientific standards. About the team The Supply Chain Optimization Technologies (SCOT) organization owns Amazon’s global inventory management systems: we decide what, when, where, and how much we should buy to meet Amazon’s business goals and to make our customers happy. We do this for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. Our systems are built entirely in-house, and are on the cutting edge in automated large-scale business, inventory and supply chain planning and optimization systems. We foster new game-changing ideas, creating ever more intelligent and self-learning systems to maximize the efficiency of Amazon's inventory investment and placement decisions. The Placement team operates within Amazon's Supply Chain Optimization Technology/Inventory Planning and Control organization. Its primary responsibility is strategically determining the placement of every inventory unit for both the Retail and FBA businesses across all Amazon websites globally. The team manages systems that optimize the vast fulfillment network, ensuring the ideal inventory placement to enhance customer and seller satisfaction on a global scale. We are open to hiring candidates to work out of one of the following locations: Toronto, ON, CAN

The position can be based in Vancouver, Canada. This is a hybrid role (3 days a week in the office) because we believe it is easier to learn, model, practice, and strengthen our culture when we’re in the office surrounded by our colleagues. We are looking for Data Science professionals to drive our analytical revolution in the Talent Acquisition (TA) space. You get the opportunity to work on a ground up rebuild of our analytical capabilities, from data ingress, to complex business transformations to end user reporting and beyond. In this role, you will invent and build on behalf of candidates, experiment and test new ideas, evangelize successes, and drive consistency. The ideal candidate is an independent Data Scientist who can source data, cleanse, analyze, refine, enrich, model, present, automate and document our business data pipelines. You will always be on the lookout for ways to optimize the information flow process, stay on top of latest trends in data warehousing and be able to coordinate and work on multiple, related projects. Key job responsibilities • Collaborate with researchers, software developers, and business leaders to define business processes and provide analytical support • Leverage code to analyze complex datasets and design, develop and evaluate data transformations to solve specific business problems • Build scalable, efficient, and automated data processes to facilitate customer-facing reporting • Automate TA processes to streamline business operations • Communicate verbally or in writing to business customers / leadership to sharing insights and recommendations We are open to hiring candidates to work out of one of the following locations: Vancouver, BC, CAN

Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. Detail Page Sourcing team in Sponsored Products org strives to help shoppers across the world to find best products for their needs. We help advertisers to boost visibility and engagement with their products in way that is highly relevant and useful for each shopper's journey arriving at Amazon product detail page. As recommendations team, we build advanced deep-learning models, large-scale machine-learning (ML) pipelines and operate at Amazon catalog scale across all Amazon marketplaces worldwide. Our team is experimentation heavy and we are always looking for new ideas to explore and innovate on behalf of shoppers and advertisers. We are looking for energetic, entrepreneurial, and self-driven science team member who would help us to invent in Deep Learning, ML recommendations space with a focus on innovation for better experience on Amazon product detail pages. Key job responsibilities As a Sr. Applied Scientist in the team, you will: - Build deep understanding of how product recommendation techniques affect key shopper engagement and advertiser performance metrics. - Work closely with product, engineering, science leaders to define and invent next set of ML (Natural Language Processing, Deep Leaning) based experiments in recommendations space. - Own deep dive into impact of your work, experiments results. We are open to hiring candidates to work out of one of the following locations: Toronto, ON, CAN

Amazon Advertising is looking for an entrepreneurial and resourceful individual to join the Advertiser Content Intelligence and Activation team as part of the Amazon Advertising Business. The team is a core product team supporting all Amazon Ads businesses and providing innovative and scalable solutions to Advertisers and Advertising content creators problems. Advertisers know that the quality and quantity of ad experience they are building are an increasingly large contributor to campaign performance. However, the science behind “what makes an effective ad experience” is at best nascent and advertisers lack the resources and technology to activate and optimize their advertising content at scale. This represents an important opportunity for Amazon to Innovate on behalf of their customers. By expanding the type of content advertisers can use (e.g AI Generated content, dynamic content) and by building a set of shared content activation and intelligence services, the team is responsible for turning advertiser content into a key driver of campaign performances and scale across all its ads products. We host hundreds of millions of assets. We build on top of large models, generate and serve multi-modal embedding for text, images, and videos. Asset metadata and intelligence serves the needs of other Amazon Ads product teams (e.g. improve the performance of Amazon Ads’ content recommendation models, Bid Optimization models, Content Optimization Models, power creative performance insights, seed data for Generative AI models, automate content moderation and spec checking). As an Applied Scientist on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE We are open to hiring candidates to work out of one of the following locations: Toronto, ON, CAN

Are you a Masters or PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. For more information on the Amazon Science community please visit https://www.amazon.science. We are open to hiring candidates to work out of one of the following locations: Toronto, ON, CAN | Vancouver, BC, CAN

Are you a PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. For more information on the Amazon Science community please visit https://www.amazon.science. We are open to hiring candidates to work out of one of the following locations: Toronto, ON, CAN | Vancouver, BC, CAN