About the position
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 Principal Applied Scientist 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 Principal Applied Scientist, 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!
Responsibilities
- Address business challenges through building compelling cases and using data to influence change across the organization
- Develop input and assumptions based on preexisting models to estimate costs and savings opportunities associated with varying levels of network growth and operations
- Create metrics to measure business performance, identify root causes and trends, and prescribe action plans
- Manage multiple high-impact projects simultaneously
- Work with technology teams and product managers to develop new tools and systems supporting business growth
- Communicate with and support various internal stakeholders and external audiences
- Implement scheduling solutions, improve metrics, and develop scalable processes and tools
- Design and implement sophisticated machine learning models for logistics optimization
- Develop complex time series forecasting algorithms for demand prediction and resource allocation
- 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
- Design and implement distributed computing solutions for processing massive logistics datasets
- Utilize cloud computing platforms (e.g., AWS) for scalable data processing and analysis
- Design and implement AI agents for autonomous decision-making in logistics processes
- Create machine learning models for customer behavior analysis and personalized delivery options
- 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
Requirements
- 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 with neural deep learning methods and machine learning
Nice-to-haves
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- 15+ years of relevant, broad research experience after PhD degree or equivalent
- Deep expertise in Machine Learning
- Proficiency in programming for algorithm and code reviews
- Strong core competency in mathematics and statistics
- Track record of successful projects in algorithm design and product development
- Publications at top-tier peer-reviewed conferences or journals
- Strong prior experience with mentorship and/or management of senior scientists and engineers
- Thinks strategically, but stays on top of tactical execution
- Exhibits excellent business judgment; balances business, product, and technology very well
- Effective verbal and written communication skills with non-technical and technical audiences
- Experience working with real-world data sets and building scalable models from big data
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
Benefits
- Equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package
- Full range of medical, financial, and/or other benefits
