Research scientists at Amazon participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for statistical modeling and time-series forecasting applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy statistical models on available data. Research and implement novel statistical approaches to add value to the business. Mentor junior engineers and scientists.
The AWS Demand Forecasting and Planning team is responsible for growing the world's largest Cloud. We forecast customer demand, build ML systems that understand customer needs, and drive utilization improvement for all AWS services.
What we own:
- Building a world-class forecasting platform that scales to handling billions of time series data in real time.
- Developing predictive customer analytics models and recommendation engines.
- Expanding inventory replenishment models and systems for each AWS service in the fast-growing AWS product portfolio.
- Finding out the optimal tradeoff between AWS service availability and fleet utilization.
- Driving fleet utilization improvement where each 1% means tens of millions of additional free cash flow.
- Automating tactical and strategic capacity planning tools to optimize for service availability and infrastructure cost.
- State of the art forecasting methodologies.
- Application of machine learning to large-scale customer analytics.
- Inventory management and supply chain management for the Cloud.
- Resource management and admission control for the Cloud.
- The internals of all AWS services.
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Our team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future
Forecasting, Statistics, Machine Learning, Optimization, Inventory Management, Supply Chain Management, AWS, Cloud, Cloud Computing, EC2, S3, EBS, DynamoDB, CloudFront, Java, C++, Object Oriented, R, Distributed Systems, High Availability, Scalability, Concurrent
- PhD or foreign equivalent in a quantitative field and five years of work or research experience in the job offered or a related occupation or a Master's Degree or foreign equivalent in a quantitative field and nine years of research or work experience in the job.
- Five years of research or work experience in the following skill(s): programming in Java, C++, Python, or equivalent programming language; and conducting the analysis and development of various statistical models for moderately complex projects in business, science, or engineering.
- Ability to communicate effectively across multiple organizations in the company
- Experience with time-series forecasting and recommendation systems
- Strong knowledge of statistics and probability
- Strong knowledge of model design, selection, and hypothesis testing
- Programming experience in Java/Scala
- Experience with SQL/noSQL databases to manage and analyze large data sets