Amazon Performance Advertising sits at the intersection of e-commerce and advertising, developing new native advertising experiences that are a critical area of strategic focus for the company. Amazon Sponsored Brands is an always-on advertising product that amplifies brand content to shoppers researching on Amazon through prominent cost-per-click ads. As we move up the purchase funnel, and create more touch points for shoppers, Sponsored Brands plays a key role in the discoverability and reach of brand content. Amazon Sponsor Brands is looking for a scientist to lead innovation for our global advertising marketplace. At the heart of our advertising business are systems for optimizing the ad marketplace involving sourcing and targeting, experimentation infrastructure, AI methods for inference and control, as well as metrics-driven closed loop optimizations.
· Design, develop, and productionize end-to-end machine learning solutions.
· Work closely with software engineers and data scientists on detailed requirements, technical designs and implementation of end-to-end solutions in production
· Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management
· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving
· Provide technical leadership, research new machine learning approaches to drive continued scientific innovation
· Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
· Help attract, lead, and mentor technical talent.
Impact and Career Growth:
· You will invent new shopper and advertiser experiences, and accelerate the pace of Machine Learning and Optimization.
· Influence 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.
· Define a long-term science vision for our ad marketplace, driven fundamentally from the needs of our customers, translating that direction into specific plans for research and applied 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.
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.
· 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
· Proven experience in optimization and multi-objective tuning modeling.
· Experience working with very large real-world data sets and building scalable models from big data.
· Effective verbal and written communications skills
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.