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.
The Brand Advertising & Shopping Experiences Team (BASE) enables brand owners on Amazon to engage shoppers who are researching and exploring product options to drive brand discovery and build stronger customer relationships. We work on a broad range of areas, including machine learning, distributed computing, large scale system design, big data pipelines, and more. Our engineers and scientists develop ML solutions and infrastructure for marketplace applications such as sourcing, relevance, ranking, and driving better utilization of our supply and demand. We develop predictive models to better match ads to shopping intent yielding to a more engaging experience for our customers. Our goal is to help brand owners connect with shoppers by positioning their promoted products.
As we move up the purchase funnel and create more touchpoints for shoppers, the Sponsored Brands business is exploring to evolve our ecosystem to fulfill the mission. The Senior Economist in Sponsored Brands is responsible for 1) innovating and evolving our ecosystem; 2) optimizing the relationship between Amazon Ads ecosystem and Amazon retail ecosystem; 3) identifying optimal pricing model for our business, upgrading our system to bring value to shopper, seller, and Amazon at a scalable way. From influencing customer-facing shopping experiences to helping suppliers grow their retail business and the auction dynamics that leverage native advertising, this role will be powering the engine of one the fastest growing businesses at Amazon.
To be successful in this role, you will need to be comfortable making innovation and designing experiments on new-to-world solutions, defining a long-term science vision for our ad marketplace, driving fundamentally from the needs of our customers, translating that direction into specific plans for research and scientists, as well as engineering and product teams. This is a role that combines science leadership, organizational ability, technical strength, product focus, and business understanding. In the immediate term, this role requires (a) addressing principles of allocation function and pricing in ad marketplace auctions, (b) making input to efficient algorithms for multiparameter optimization and AI control methods to find operating points for the ad marketplace auctions and to evolve them, and (c) develop science talent around learning, Economics, and optimization for WW ads.
The ideal candidate will be an independent thinker who can make convincing, information-based arguments. With a strong bias for action, this individual will be able to work equally well with science, engineering, Economics, and business teams. This person will have very sound judgment and be able to recruit and groom high caliber talent. Professional traits that are not unique to this position, but necessary for Amazon leaders:
· Exhibits excellent judgment
· Hires great people. Develops great people.
· Has relentlessly high standards
· Expects, enables and requires innovation of all teams
· Thinks big and has convictions
· Results oriented
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
· PhD in Economics
· 5+ years experience investing the feasibility of applying scientific principals and concepts to business problems and products
· Programming in one or more statistics-oriented languages (Stata, Python, R).
· Experience in causal inference and machine learning.
· Ability to communicate relevant scientific insights from data to senior business leaders.
· Applicants with considerably more experience, including mid-career, are also strongly encouraged.
· Experience implementing machine learning methods.
· Proficiency in Spark-Scala, Py-Spark, etc.
· Experience building and bringing high impact statistical models to production, at scale.
· Experience effectively collaborating with software engineers and applied scientists.