Our mission: To be Earth's most customer-centric company.
Amazon’s eCommerce Foundation (eCF) organization provides the core technologies that drive and power Amazon’s Consumer, Digital, and other businesses. Millions of customer page views and orders per day are enabled by the systems eCF builds from the ground up.
CloudTune, within eCF, empowers growth and business agility needs by automatically and efficiently managing AWS capacity and business processes needed to safely meet Amazon’s customer demand. CloudTune serves its primary customers, internal software teams, through forecast driven automation of cost controllership, capacity management and scaling. We predict expected load, and drive procurement and allocation of AWS capacity for new product launches and high velocity events like Prime Day and Cyber Monday.
CloudTune is looking for an experienced Applied Scientist to join our forecasting team. The team develops sophisticated algorithms that involve learning from large amounts of past data, such as actual sales, website traffic, merchandising activities, promotions, similar products and product attributes, in order to forecast the demand for our compute infrastructure. These forecasts are used to determine the level of investment in capital expenditures, promotional activity, engineering efficiency projects and determining financial performance.
As a scientist in the CloudTune Forecasting team, you will work with other scientists, product managers, software engineers, and data engineers on a variety of important applied machine learning problems in the area of time series modeling. You will work on statistical problems with a high level of ambiguity. You will analyze and process large amounts of data, develop new algorithms and improve existing approaches based on statistical models, machine learning algorithms and big data solutions to automatically scale Amazon’s compute infrastructure, optimizing the balance between availability risk and cost efficiency for all of Amazon businesses.
Key job responsibilities
Process and analyze large data sets, mining additional data sources as needed
Analyze compute scaling metrics to identify business drivers that influence infrastructure expenditures
Build mathematical models to represent demand forecasting at various levels.
Prototype these models by using high-level modeling languages such as R or in software languages such as Python. A software team will be working with you to transform prototypes into production.
Create, enhance, and maintain technical documentation, and present to other scientists and business leaders.
• MSc. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
• 4+ years of hands-on experience in predictive modeling and machine learning or equivalent PhD degree.
• Strong working knowledge of data cleaning, machine learning, and analytics techniques.
• Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives
• Comfortable working in a fast paced, highly collaborative, dynamic work environment
• At least 2+ years hands on experience programming in Python, R, Java, C#, C++ or other similar programming languages
• Significant peer reviewed scientific contributions in relevant field.
• Extensive experience applying theoretical models in an applied environment.
• Expert in more than one more major programming languages (C++, Java, or similar) and at least one scripting language (Perl, Python, or similar).
• Strong fundamentals in problem solving, algorithm design and complexity analysis.
• Strong personal interest in learning, researching, and creating new technologies with high commercial impact.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.