EU Prime Economist

Amazon

Rives de Clausen, Luxembourg

Ref: 815409

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The European Amazon Prime team is looking for a talented Economist to help manage our ever growing information needs and support analysis of increasingly complex business questions. At Amazon Prime, understanding customer data is paramount to our success in providing customers with relevant and enticing benefits such as fast free shipping, instant videos and music, in the US as well as an expanding number of international marketplaces. At Amazon you will be working in one of the world's largest and most exciting big-data environments. The Economist role occupies a unique space at the intersection of technology, machine-learning, econometrics, large-scale scientific computing, social science, and product management.

As an Economist within Amazon Prime, you will work with our world-class business, economics, data science and engineering, and software development teams to propose and estimate novel statistical and econometric models to directly inform strategic decisions about characteristics of the Amazon Prime membership, including what membership prices, benefits, and benefit content deliver the most value for our customers around the world. You will solve these problems using structural econometrics, causal inference, machine learning, and massively parallelized scientific computing, and work closely with our software development team to automate these models at scale in distributed computing infrastructures such as Apache Spark. This position is unique in its exposure to senior members of the Prime team and other Amazon business units.

The successful candidate will be familiar and comfortable with building, estimating, and defending causal statistical models using software such as R, Python, or STATA, with a willingness to learn causal inference and structural econometrics and experience creating production systems. Knowledge of SQL, machine learning, and large scale scientific computing using distributed computing infrastructures such as Spark-Scala or PySpark would be a plus. Additionally, the candidate will need to demonstrate the ability to communicate complicated concepts clearly to business leaders and other scientists, the desire to develop large business impacts for customers using statistics and econometrics, and strong execution and completion skills for major, highly-visible strategic projects under fast-paced business deadlines. The role involves both building data products, i.e. automated and productionized statistical models at scale, as well as supporting ad-hoc strategic decisions.

The successful candidate will have the opportunity to work directly with our Prime teams in Luxembourg, London, Paris, Milan, Munich and Madrid as well a partner closely with our WW Teams in Seattle, Japan and India.

BASIC QUALIFICATIONS

  • PhD in Statistics, Computer Science, Economics, Operations Management or a related field
  • Proven experience in building statistical models using R, Python, STATA, or a related software (especially, discrete choice modeling), with a willingness to learn and develop additional skills in causal inference, structural econometrics, machine learning, large-scale scientific / distributed computing.
  • Proven record of bringing high impact statistical models to production, at scale
  • Willingness to learn Spark-Scala and/or PySpark
  • Ability to work effectively within an interdisciplinary science team of economists, applied scientists, software engineers, and data engineers
  • Ability to communicate relevant scientific insights from data to senior business leaders, financial analysts, and product managers

PREFERRED QUALIFICATIONS

  • 2-3 years of post-PhD experience
  • Proficiency in Spark-Scala or Py-Spark
  • Extensive theoretical statistical training, including the ability to carefully adapt/modify existing statistical tools to accommodate new applied use cases
  • Experience with utility-theory based discrete choice-modeling
  • One or more publications in peer-reviewed statistical journals
  • Experience in implementing modern machine-learning methods (e.g., boosted regression trees, random forests, neural networks
  • Passage of SDE I Amazon bar

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