Sr. Data Scientist, Ops Comp Engineering, Analytics 'N Science
Amazon
DESCRIPTION
Do you love data? Do you have a passion for learning new technology? Do you want to make large scale impact?
Join the Operations Compensation team. We are an analytics team that supports Amazon’s Global Operations in evaluating the employee life cycle. We use data to influence the way Amazon grows and scales our business by advising on compensation strategies for over a million employees worldwide.
We are looking for a Sr. Data Scientist to help us extract meaning from large volumes of data to make business decisions. This role will help establish the science vision, strategy, and roadmap for our team and work with senior leadership in the organization to make it happen. They will be a senior technical advisor for the team. The right candidate knows and loves working with new ever-evolving technology, can model multi-dimensional datasets, and can partner effectively with business leaders to answer key questions. They are analytical and creative. They work efficiently and routinely to deliver the optimal solutions for our customers. They have performed complex and business critical analyses that led to high impact for their business. They show strong judgement in making technical trade-offs between short- and long-term business objectives.
Key job responsibilities
In this role, you will have the opportunity to display and develop your skills in the following areas:
• Engage directly with Amazon executive leadership to recommend business intelligence solutions.
• Develop forecasting models or improve existing approaches using scalable techniques.
• Partner with customers to define the data elements and data structures that the team should leverage to enable analytical capabilities.
• Design and implement applications that enable business leaders to build their own scenarios using pre-defined forecasting models.
• Collaborate with Economists on statistical models and techniques to conduct analysis.
• Closely work with engineering teams to build scalable, efficient systems that implement prototypes in production.
• Make recommendations for new methods, techniques, and strategies to drive success of our organization.