Want to work on machine learning and big data to power a multi-billion dollar advertising product?
Amazon is investing heavily in building a world class advertising business to define and deliver a collection of advertising products that drive long-term customer value. Our team – Sponsored Brands (SB) - owns an auction based, keyword targeted, mid funnel advertising product designed for brand owners. We empower brands of all shapes and sizes to attract shoppers in the research and consideration phases of their shopping journey, through visually stunning, inspiring and relevant sponsored shopping experiences. In doing so, we deliver billions of ad impressions and millions of clicks daily, but are only just getting started.
· Partnering with economists and senior team members to drive science and implement technical solutions using machine learning and econometrics
· Collaborate with Data Engineers to help build data systems and metrics that continually improve data quality
· Develop science-driven algorithms that yield robust recommendations along pricing, bidding for advertisers
· Contribute to building a scalable experimental framework that help stakeholders make data-driven informed decisions
· Communicate verbally and in writing to senior leaders with various levels of technical knowledge, educating them about your approach, as well as sharing insights and recommendations
· Bachelor's Degree
· 3+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
· 2 years working as a Data Scientist
· Master’s or PhD degree in Statistics, Applied Math, Economics, Computer Science, Machine Learning or a related quantitative field with at least 2 years of working experience as a Data Scientist
· Experience using AWS technologies such as Redshift, S3, EMR and data pipelines and scaling models into production
· Experienced in Machine Learning
· Experience working with econometric models on causal impact questions
· Experience in designing and analyzing large scale experiments