We develop scalable and robust state-of-the-art data/analytics/ML and automation solutions that involve learning from different data sources and advanced descriptive, diagnostic, predictive prescriptive and cognitive models. With better forecasts we drive down supply chain costs, enabling the offer of lower prices and better in-stock selection for our customers.
In this role, you will have an opportunity to both develop advanced scientific solutions, and drive critical customer and business impacts. You will play a key role to drive end-to-end solutions from understanding our business requirements, exploring a large amount of historical data and ML models, building prototypes and exploring conceptually new solutions, to working with partner teams for prod deployment. You will collaborate closely with scientists, engineering peers as well as business stakeholders. You will be at the heart of a growing and exciting focus area for Amazon Devices and Services.
You are an individual with outstanding analytical abilities, excellent communication skills, and are comfortable working with cross-functional teams and systems. You will be responsible for researching, prototyping, experimenting, analyzing predictive models and developing smart automation solutions.
Key job responsibilities
- Research and develop new methodologies for demand forecasting, alarms, alerts and automation with advanced models and methods
- Improve upon existing methodologies by adding new data sources and implementing model enhancements
- Drive scalable solutions
- Create and track accuracy and performance metrics (both technical and business metrics)
- Create, enhance, and maintain technical documentation, and present to other scientists, engineers and business leaders
- Drive best practices on the team
About the team
The team is a focused science team looking for help with validating the technical decisions we are making and figuring out how to best solve our internal and partner’s needs.
BASIC QUALIFICATIONS
- Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience building machine learning models or developing algorithms for business application
- Experience programming in Java, C++, Python or related language
- Proficiency in model development, model validation and model implementation.
- Strong programing skills in Python, R or Scala.
PREFERRED QUALIFICATIONS
- Experience implementing algorithms using both toolkits and self-developed code
- 2+ years of experience of building machine learning models for business application.
- Experience with time series modeling and machine learning forecasting.
- Good knowledge of SQL, Redshift and AWS infrastructure.
- Experience with data management and/or high performance computing.
- Experience working in command-line Linux environments.
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 $129,400/year in our lowest geographic market up to $212,800/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.