Senior Data Scientist, Rapid & Rural Logistics (R2L) Science & AI

Key job responsibilities

We are looking for an experienced and curious data scientist with effective superior analytical skills to inform the data science charter of the team. This position is critical in helping us learn more about our data and finding opportunities to delight customers with data driven insights and machine learning models. The Data Science and Analytics team owns data science, data engineering, and business intelligence. You will be supporting multiple business and technical stakeholders with high velocity analytics.

This role is uniquely positioned in the team as we have a growing need for looking around corners, prioritizing opportunities using data driven insights, and finding solutions to these opportunities using different machine learning techniques and causal inference models.

You will be diving deep in our data and have a strong bias for action to quickly produce high quality data analyses with clear findings and recommendations. As part of our journey to learn about our data, some opportunities may be a dead end and you will be balancing unknowns with delivering results for our customers.

A day in the life

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Benefits

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

Basic Qualifications

• 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 4+ years of data scientist experience
• Experience with statistical models e.g. multinomial logistic regression

Preferred Qualifications

• 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
• Experience managing data pipelines
• Experience as a leader and mentor on a data science team