Description

Revolutionize logistics through groundbreaking data science at Amazon! We're seeking an innovative problem-solver who will transform complex global logistics challenges using advanced analytics and artificial intelligence. Your work will directly impact how millions of packages move worldwide, leveraging cutting-edge machine learning, Large Language Models (LLMs), and predictive technologies to optimize our intricate delivery networks.

Our team empowers data-driven decision-making by developing sophisticated analytical solutions that bridge operational insights with strategic vision. You'll have the opportunity to design scalable models, implement advanced AI technologies, and create transformative recommendations that reshape logistics efficiency at a global scale.

Key job responsibilities
• Build sophisticated machine learning models and production pipelines to analyze complex logistics data
• Develop advanced AI solutions using Python, AWS tools, and emerging technologies like Large Language Models
• Synthesize information from diverse data sources to generate meaningful, actionable business insights
• Collaborate with cross-functional teams to translate complex technical findings into strategic recommendations
• Design and implement optimization strategies that enhance network performance and operational efficiency


A day in the life
As a Data Scientist in our R2L team, you'll immerse yourself in intricate data ecosystems, uncovering hidden patterns and opportunities that can revolutionize package delivery. Your daily work will involve exploring massive datasets, developing innovative analytical approaches, building production pipelines and transforming raw information into strategic intelligence.

About the team
We are a dynamic collective of data enthusiasts dedicated to optimizing Amazon's logistics infrastructure. Our team operates at the intersection of technology, operations, and strategic innovation, working collaboratively to create more intelligent, responsive, and efficient delivery systems.