Work with quantitative modelers within measurement and big data experts at AWS to deliver and operate services; be responsible for overall system architecture, scalability, reliability, and performance.
Understanding of Agile way of working, software feature development (creating user stories and acceptance criteria), and using product analytics to improve existing features and create new features (required).
The goal of this team is to predict trends, find insides and produce actionable, trustworthy recommendations and decisions for Office 365 operation and modern support, which is curried on through an analytical modelling process based on machine learning by processing structured, semi-structed and unstructured data.
Receive consideration for employment without regard to race, color, gender, sexual orientation, gender identity or expression, religion, national origin, marital status, age, disability, veteran status, genetic information, or any other protected status.
As a Data Scientist, you will be part of the analytics team behind Amazon Go and have a direct impact of customer experience as we grow and scale. You will leverage Amazons heterogeneous data sources and large-scale computing resources to accelerate our development.
Analyzing and understanding large amounts of the companys historical business data for specific instances of risk or broader risk trends; design, development and evaluation of highly innovative models for risk management.
This position is on a new team that you will be joining with a startup mentality. You must enjoy working on complex software systems in a customer-centric environment and be passionate not only about building good software but also ensuring that the same software achieves its goals in operational reality.
To provide technical expertise and execute research strategies in support of mostly late stage translational projects focused on expedited evaluation and translation of therapies for diabetic kidney disease.
The role would require an ability to understand large scale systems, analyze large volumes of data to determine areas of improvement, formulate solutions, drive projects, build prototypes, and launch to production.