DESCRIPTION

The Decision, Science and Technology team (DST) part of the Global Reliability in the maintenance space Team (GRT) drives the Condition, Predictive and Preventive Maintenance for Amazon sites around the globe. Our mission is to leverage the use of data, science, and technology to improve the efficiency of GRT maintenance activities, reduce costs, increase safety and promote sustainability while creating frictionless customer experiences.

As a Data Scientist II, you'll lead solution review, metric development, and scheduling optimization. You'll contribute to predictive maintenance, apply advanced analytics and machine learning, and collaborate across departments. Your role involves ensuring data quality, developing governance policies, and driving continuous improvement. You'll leverage statistical techniques to solve complex business problems, develop predictive models, and stay current with industry trends. Your expertise will be crucial in enhancing operational efficiency, supporting strategic decision-making, and contributing to the company's data science strategy and roadmap.

You will connect with world leaders in your field and you will be tackling customer's natural language challenges by carrying out a systematic review and analysis of existing solutions. The appropriate choice of AI methods and their deployment into effective tools will be the key for the success in this role.

This role offers an exciting opportunity to impact critical business operations through data-driven decision-making.

Key job responsibilities
- Lead the review and optimization of existing analytics solutions to maximize business value
- Develop and implement strategic metrics and KPIs to support decision-making processes
- Drive improvements in scheduling optimization and resource allocation
- Contribute to predictive maintenance programs using advanced analytical techniques
- Design and deploy machine learning models to solve complex business challenges
- Collaborate across departments to deliver data-driven solutions
- Establish and maintain data quality standards and governance frameworks