Data Scientist II Amazon
Seattle, WA

Job Description

AWS Insight is looking for a Data Scientist to help develop sophisticated algorithms and models that involve analyzing and learning from over 540 billion customer cost, usage, and utilization events daily. We use this data to generate recommendations and forecasts for customers to help them better understand and optimize their AWS costs and usage and reduce the complexity of managing their cloud costs. Our team's vision is to be the world's authoritative provider of AWS computing insight, where customers can understand, control and optimize usage of AWS products. We sit at the nexus of all AWS services and interact directly with end-customers, and we build relationships with teams across AWS to ensure that we offer a secure and reliable customer experience that builds trust with our customers and provides them with intelligent insights.

As a successful data scientist in AWS Insights, you will be responsible for understanding and mining the large amount of data, and developing recommendations that will help improve the accuracy and relevance of our forecasting and recommendations models. You will work closely with talented data scientists, software engineers, and business groups to build enhance existing models and build new models that solve challenging customer problems. You will work with the engineers to drive implementation of the proposed models and establish testing strategies to validate the models before and after they are put into production. On top of that, you are an analytical problem solver who enjoys diving into data, are excited about investigating and developing algorithms, and can influence technical teams and business stakeholders to solve real-world customer problems.

Key responsibilities:

* Improving upon existing forecasting statistical or machine learning methodologies by developing new data sources, testing model enhancements, running computational experiments, and fine-tuning model parameters for new forecasting models
* Supporting decision making by providing requirements to develop analytic capabilities, platforms, pipelines and metrics then using them to analyze trends and find root causes of forecast inaccuracy
* Formalizing assumptions about how demand forecasts are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them
* Translating forecasting business requirements into specific analytical questions that can be answered with available data using statistical and machine learning methods; working with engineers to produce the required data when it is not available
* Communicating verbally and in writing to business customers with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
* Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical and machine learning models and algorithms