We are looking for a dynamic, self-starter Senior BIE for IES Shopping - Analytics and Science Team (AST) to guide Amazon Bazaar program in India with analytics and data-driven insights. You will be working in one of the world's largest and most complex data warehouse environments. You must have a track record of churning out insights and make actionable recommendations to audiences of varying technical aptitude that directly impact organizational strategic decisions and priorities. Being able to thrive in an ambiguous, fast-moving environment and prioritizing work is essential, as is a mind for innovation and learning through rapidly evolving and new technologies. This role provides an opportunity to develop original ideas, approaches, and solutions in a competitive and ever-changing business climate.

Key job responsibilities
- Conduct deep dive analyses of business problem statements and formulate conclusions and recommendations to leadership
- Share written recommendations and insights for key stakeholders that will help shape organizational strategic decisions and priorities
- Contribute to the design, implementation, and delivery of BI solutions for complex and ambiguous problems
- Simplify and automate reporting, audits, and other data-driven activities
- Partner with other BIEs to enhance data infrastructure, data availability, and broad access to customer insights
- Develop and drive best practices in data integrity, consistency, analysis, validations, and documentation
- Learn new technology and techniques to meaningfully support internal stakeholders and process innovation

About the team
IES Shopping - Analytics and Science Team (AST) has a vision to embed a data culture deeply in our IES Shopping Experience organization, fostering invention through insights, and building a robust data architecture to support business needs. We spin the insights flywheel by growing a pool of bar-raisers and diverse data professionals, which empowers us to continuously enhance our data capabilities, holistically covering disciplines of Data Engineering, Business Intelligence, Analytics, and Machine Learning.