Are you passionate about developing new state-of-the-art measurement approaches at Petabyte scale? Amazon Advertising is one of Amazons fastest growing businesses, and we are leveraging our unique data, the latest machine learning methods and big data technologies to better understand how Amazons marketing influences customer behavior. We are looking for a Senior Applied Scientist to develop new systems and methods in the most challenging and data rich areas of marketing. We need an expert in experimental statistics, machine learning or causal inference to design advanced new models with our world class data systems. Dozens of Amazon businesses use Amazons ad tech for their marketing objectives, driving more than $1 B of marketing investments through Ads services and tools. As part of the 1 PM team, this role will partner with a dedicated engineering team measuring the impact Amazon's marketing and identifying opportunities for optimization at scale. We drive initiatives to make smarter marketing decisions and improve the relevance of advertising to our customers. We move away from industry standard measurement systems and build sophisticated and insightful decision engines. We enable massive advertising programs, generating billions of impressions decorated with rich representations of customer state. The major challenges we are solving include integrating petabyte-scale distributed retail systems into a singular service to synthesize e-commerce data into measurement and optimization models. The successful candidate will have a causal inference background, a start-up mentality, an appreciation for white-space, and success solving problems with large data sets. Key job responsibilities Scientists at Amazon are expected to develop new techniques to process large data sets and contribute to design of automated systems. Apply ML, statistics or econometrics knowledge to develop and analyze prototype models. Design and analyze data from large-scale online experiments in order to validate prototype models Collaborate with scientists across teams in peer-review processes , publishing research in internal forms and industry conferences Partner closely with product and engineering teams to develop new measurement systems and translate prototype models to production. Establish scalable, efficient, and automated processes for large scale model development, validation, and implementation. Research and experiment with novel statistical modeling approaches. Basic Qualifications- 4 years of building machine learning models for business application experience- PhD, or Master's degree and 6 years of applied research experience- Experience programming in Java, C , Python or related language- Experience with neural deep learning methods and machine learning. Preferred Qualifications- Experience with modeling tools such as R, scikit-learn, Spark ML - Lib, Mx.Net, Tensorflow, numpy, scipy etc.- Experience with large scale distributed systems such as Hadoop, Spark etc.- Knowledge of causal inference.