CloudTune, in partnership with Region Flexibility, is driving an SDO-wide program to diversify our use of AWS regions beyond DUB, IAD, and PDX regions. The objective of the Diversify AWS Region Usage (DARU) program is to mitigate the risk of capacity concentration by encouraging teams to design workloads that are region-flexible, utilize AWS automation such as Flexible Fleets to access multiple capacity pools, and optimize workload placement so SDO efficiently utilizes AWS. This is a strategic, highly visible, multi-year program which spans all Amazon business.
CloudTune is looking for an experienced Applied Scientist to join our forecasting team and support DARU program. 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 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 Senior Scientist in CloudTune, you will work with other scientists, software engineers, data engineers, and product managers 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 statistical models and drive scalable solutions for multi-year capacity demand forecasting horizons
• Prototype these models by using high-level modeling languages such as R or Python
• Create, enhance, and maintain technical documentation, and present to other scientists and business leaders
BASIC QUALIFICATIONS
- 5+ years of building machine learning models for business application experience
- PhD, or Master's degree and 7+ years of building machine learning models or developing algorithms for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field
- 7+ years of industry or academic research experience
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.