Have you ever wondered how that Amazon box with the smile arrives so quickly, where it came from, and how much it cost Amazon to deliver? The WW Amazon Logistics, Business Analytics team manages the delivery of tens of millions of products every week to Amazon's customers, achieving on-time delivery in a cost-effective manner.

We are seeking an enthusiastic, customer-obsessed Sr. Manager Research Science with strong analytical skills to join our team. This role is crucial in optimizing Amazon's vast delivery network and will have significant impact on the customer experience, particularly in the final phase of delivery.

As a Sr. Manager Research Science, you will:

1. Address business challenges through building compelling cases and using data to influence change across the organization
2. Develop input and assumptions based on preexisting models to estimate costs and savings opportunities associated with varying levels of network growth and operations
3. Create metrics to measure business performance, identify root causes and trends, and prescribe action plans
4. Manage multiple high-impact projects simultaneously
5. Work with technology teams and product managers to develop new tools and systems supporting business growth
6. Communicate with and support various internal stakeholders and external audiences
7. Implement scheduling solutions, improve metrics, and develop scalable processes and tools

The ideal candidate will have:

• PhD in Operations Research, Statistics, Engineering, or Supply Chain Management
• Extensive experience in operations research and data-driven decision making
• Strong analytical and problem-solving skills
• Robust program management and research science skills
• Ability to work with a team and make independent decisions in ambiguous environments
• Customer-obsessed mindset with a focus on improving the Amazon delivery experience

This role offers the autonomy to think strategically and make data-driven decisions from day one. Join us in shaping the future of e-commerce delivery and addressing the core challenges in our world-class operations space!

Key job responsibilities
1.

Advanced Modeling and Algorithm Development:

• Design and implement sophisticated machine learning models for logistics optimization
• Develop complex time series forecasting algorithms for demand prediction and resource allocation

AI and Machine Learning Integration:

• Architect and deploy AI-powered systems to enhance decision-making in logistics operations
• Implement deep learning techniques for image recognition in package sorting and handling
• Develop reinforcement learning algorithms for adaptive scheduling and resource management

Big Data Analytics and Processing:

• Design and implement distributed computing solutions for processing massive logistics datasets
• Utilize cloud computing platforms (e.g., AWS) for scalable data processing and analysis

AI-Driven Workflow Optimization:

• Design and implement AI agents for autonomous decision-making in logistics processes
• Create machine learning models for customer behavior analysis and personalized delivery options

Software Development and System Architecture:

• Write efficient, scalable code in languages such as Python, Java, or C++
• Develop and maintain complex software systems for logistics optimization
• Stay at the forefront of AI and ML research
• Publish research findings in top-tier conferences and journals

About the team
We are Amazon's Last Mile Science and Analytics team, dedicated to improving e-commerce delivery. We work to optimize our vast network, forecast demand using machine learning, and enhance route efficiency. Our efforts focus on developing innovative delivery methods, applying AI to solve complex problems, and conducting geospatial analysis. We create simulations to refine processes and plan capacity effectively. Operating globally, we strive to develop adaptable solutions for diverse markets. We aim to advance logistics science, continually improving speed, efficiency, and customer satisfaction, in support of Amazon's mission to be Earth's most customer-centric company.

Basic Qualifications

• 10+ years of building machine learning models or developing algorithms for business application experience
• PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 10+ years of industry or academic research experience
• Knowledge of programming languages such as C/C++, Python, Java or Perl
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

Preferred Qualifications

• PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
• Knowledge of deep learning, machine learning and statistics
• 4+ years of scripting, programming, or security code review in a common language, such as Python, Java or C++ experience
• Knowledge of mathematical/statistical/physics fundamentals
• 8+ years of successful technology products work from ideation through launch experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience in science or engineering team management
• Experience establishing successful partnerships with internal and external teams to execute tactical initiatives or equivalent
• Experience shaping business strategy for technical products or services for large enterprises or partners
• Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership
• 4+ years of data science, business analytics, business intelligence, or similar experience in big data environments experience
• Experience with modeling tools such as R, scikit-learn, Spark MLLib, Mx Net, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.
• 15+ years of relevant, broad research experience after PhD degree or equivalent.

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