Company Overview

Amazon Shipping is a dynamic and innovative team that is revolutionizing the way packages move around the world. As an Applied Scientist in this team, you will have the opportunity to work on multiple challenging machine learning problems that impact our ability to plan and execute package movements. Our mission is to make Amazon the most customer-obsessed company on earth. We are committed to delivering fast, reliable, and affordable shipping options to our customers. To achieve this goal, we need talented individuals like you who can help us develop cutting-edge machine learning solutions that drive business results. As an Applied Scientist in Amazon Shipping, you will be part of a diverse team of data scientists, software development engineers, and business intelligence engineers who are passionate about using data and analytics to drive business decisions. We are looking for talented individuals who are passionate about machine learning and have a strong desire to learn and grow with our team. If you are excited about working on complex machine learning problems and have a passion for innovation, then this role is perfect for you!

Required Skills and Qualifications

• PhD or Master's degree in Computer Science, Statistics, Mathematics, or related field
• Strong programming skills in languages such as Java, C++, Python, or related languages
• Experience in machine learning algorithms and techniques
• Strong understanding of statistical concepts and probability theory
• Excellent communication and collaboration skills

Benefits

• Competitive salary and benefits package
• Opportunities for career growth and professional development
• Collaborative and dynamic work environment
• Access to cutting-edge technologies and tools
• Flexible work arrangements and remote work options

Job Description

• Develop and deploy machine learning models that improve package movement efficiency and reduce costs
• Collaborate with cross-functional teams to identify business opportunities and develop data-driven solutions
• Analyze large datasets to identify trends and patterns that inform business decisions
• Design and implement experiments to test hypotheses and evaluate model performance