Applied Scientist

Job description

DESCRIPTION

How can we build talent pipelines to support Amazon's growth? What is important for talent to be successful at Amazon? How can we better predict talent movement (promotions, transfers, attrition) and quantitatively identify the root causes? When does internal movement make the biggest impact on Amazon talent? These are among the most important questions at Amazon today.
As an Applied Scientist on the Worldwide Operations Talent Products Team you will have an opportunity to collaborate with a team of economists, data engineers, UI developers, and software development engineers in answering these questions and developing products to manage these insights at scale. This team will shape the strategic direction and inform talent decisions at the highest levels of Worldwide Operations, helping us to become the world's most scientific and technically proficient Human Resource organization.

The successful candidate will have experience working cross-functionally where both science and technology converges to formalize problem definitions from ambiguous requirements, build econometrics models using Amazon's data systems, and develop consumer grade products for use by Amazonian business leaders. They will have experience interacting with a large number of internal stakeholders and able to communicate clearly and effectively to all levels of the company, both in writing and in meetings.

Major responsibilities:

· Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key features and processes.
· Work closely with software engineering teams to drive new feature creation
· Work closely with stakeholders to optimize various business operations
· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
· Track general business activity and provide clear, compelling management reporting on a regular basis
· Research and implement novel statistical approaches

#LTM

Desired profile

BASIC QUALIFICATIONS

· MS. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
· 1+ years of hands-on experience in predictive modeling and analysis
· Strong algorithm development experience
· Skills with Java, C++, or other programming language, as well as with R, MATLAB, Python or similar scripting language