** This job can be based in Seattle or Palo Alto **
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!
Sponsored Products helps merchants, retail vendors, and brand owners succeed via native advertising that grows incremental sales of their products sold through Amazon. The Sponsored Products Ad Marketplace organization optimizes the systems and ad placements to match advertiser demand with publisher supply using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with products - with a high relevance bar and strict latency constraints. Our goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.
As a Senior Applied Scientist for the Sponsored Products Detail Page Allocation and Pricing team, you will own systems which make the final decision on which ads to show, where to place them on the page and how many ads to place. This also includes selection of various themes that would appear in detail pages. This is a challenging technical and business problem, which requires us to balance the interests of advertisers, shoppers, and Amazon. You'll develop a data-driven product strategy to define the right quantitative measures of shopper impact, using this to evaluate decisions and opportunities. You'll balance a portfolio of pragmatic and long-term investments to drive long term growth of the ads and retail businesses.
As a Senior Applied Scientist on this team you will:
· Act as the technical leader in Machine Learning and drive full life-cycle Machine Learning projects.
· Develop real-time algorithms to allocate billions of ads per day in advertising auctions.
· Lead technical efforts within this team and across other teams.
· Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.
· 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.
· Work closely with software engineers to assist in productionizing your ML models.
· Research new machine learning approaches.
· Recruit Applied Scientists to the team and act as a mentor to other Scientists on the team.
Impact and Career Growth:
In this role you will have significant impact on this team as well as drive cross team projects that consist of Applied Scientists, Data Scientists, Economists, and Software Development Engineers. This is a highly visible role that will help take our products to the next level. You will work alongside many of the best and brightest science and engineering talent and the work you deliver will have a direct impact on customers and revenue!
Why you love this opportunity:
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.
Team video ~ https://youtu.be/zD_6Lzw8raE
· PhD degree with 4 years of applied research experience or a Masters degree and 6+ years of experience of applied research experience
· 3+ years of experience in building machine learning models for business application
· Experience programming in Java, C++, Python or related language
· Breadth and depth in machine learning, from a theoretical and real-world applications perspective.
· Experience productionizing machine learning models.
· Experience in building large-scale machine-learning models and infra for online recommendation, ads ranking, personalization, or search, etc.
· Technical leadership in machine learning.
· Published research work in academic conferences or industry circles.
· Effective and crisp verbal and written communication skills with non-technical and technical audiences.
· Experience working with very large real-world data sets and building scalable models from big data.
· Demonstrated successful industrial experience, drives results.
· Thinks strategically, but stays on top of tactical execution.
· Exhibits excellent business judgment; balances business, product, and technology very well.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us