Amazon is building a world class advertising business and defining and delivering a collection of self-service performance advertising products that drive discovery and sales of merchandise. 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.
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.
The Supply team (within Sponsored Products) is looking for an Applied Scientist to join a fast growing team with the mandate of creating high-visiblity ad experiences that engage shoppers at the point of entry on Homepages and elevate the experience for hundreds of millions customers worldwide. The Applied Scientist will take end-to-end ownership of driving new product/feature innovation by applying advanced statistical and machine learning models. The role will handle peta bytes of unstructured data (images, text, videos) to extract insights into what meta data can be useful for us to highlight to simplify purchase decisions, and propose new experiences that increase shopper engagement.
As an Applied Scientist in Sponsored Products, you will:
· Conduct hands-on data analysis, build large-scale machine-learning models
· Work closely with software engineers to help deploy models into production.
· Run regular A/B experiments, gather data, perform bespoke statistical analysis, and communicate impact to senior science and product leaders
· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving
· Provide technical leadership and research new machine learning approaches that drive scientific innovation.
· Work closely with product management to proactively identify opportunities where cutting edge science and research can help improve customer experience.
· Collaborate with other scientists, mentor junior scientists (both within and outside of this team), and publish innovative work through multiple avenues within the Amazon Science Community.
· Be an active member of the Amazon-wide Machine Learning Community, participating in internal and external conferences and events.
· Help attract and recruit technical talent.
Why you love this opportunity
Amazon is investing heavily in building a world-class advertising business. This team is responsible for defining and delivering a collection of self-service performance 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 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 fundamentally 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
· PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field
· 2+ years of experience of building machine learning models for business application
· Experience programming in Java, C++, Python or related language
· Breadth and depth in machine learning algorithms and best practices.
· 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