Senior Applied Scientist

6日前

Tokyo Amazon Japan G.K. - A43 Full time
We are seeking a data scientist or machine learning engineer to build production-grade models for Amazon's shopping and delivery experience. Key responsibilities include formulating business problems into scientific ones, building large-scale data pipelines, training and evaluating ML models, and automating model inference on AWS infrastructure. The ideal candidate has experience with Python, SQL, and distributed computing frameworks, and is proficient in machine learning techniques for tabular data. We are open to hiring candidates to work out of Tokyo, Japan or other locations. The role requires a strong understanding of statistical analysis and machine learning techniques, as well as experience in developing and implementing machine learning models in production. The salary information can be provided individually prior to the 1st interview. The candidate will work closely with Product, Finance, and Engineering teams to drive change at Amazon. We focus on solving long-term, ambiguous, and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic/econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with JP-EU-and US-based interdisciplinary teams. The ideal candidate is a self-starter who enjoys ambiguity in a fast-paced and ever-changing environment. They are extremely proficient in Python, SQL, and distributed computing frameworks. They have excellent understanding of how machine learning models work under the hood. In addition, they may have worked with AWS infrastructure and causal uplift modeling techniques. They think big on the next game-changing opportunity but also dive deep into every detail that matters. They insist on the highest standards and are consistent in delivering results. We are open to consider high-potential candidates with less experiences for a more junior position. Key job responsibilities include:

  • Work with Product, Finance and Engineering to formulate business problems into scientific ones

  • Build large-scale data pipelines for training and evaluating the models using PySpark/SparkSQL

  • Extensively clean and explore the datasets

  • Train and evaluate ML models in a robust manner

  • Design and conduct live experiments to validate model performance

  • Automate model inference and monitoring and on AWS infrastructure


BASIC QUALIFICATIONS



  • PhD, or Master's degree and 5+ years of applied research experience


PREFERRED QUALIFICATIONS



  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

  • Experience with large scale distributed systems such as Hadoop, Spark etc.

  • Strong understanding of statistical analysis (hypothesis testing and experiment design) and machine learning techniques for tabular data

  • Experience in developing and implementing machine learning models for tabular data in production

  • Publications in top-tier machine learning conferences

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