An Amazon employee is seen making a delivery while an electric delivery van is parked behind him on a residential street in Los Angeles
When Amazon announced it would purchase 100,000 custom electric delivery vehicles, a team of scientists within the Amazon Logistics Research organization took on the challenge of determining the best strategy for deploying them.
About Amazon

The science of operations planning under uncertainty

How the Amazon Logistics Research Science team guides important decisions related to last-mile delivery.

When Amazon announced it would purchase 100,000 custom electric delivery vehicles as part of The Climate Pledge, a team of scientists within the Amazon Logistics (AMZL) Research organization took on the challenge of determining the best strategy for deploying them. Based on sophisticated models that simulate Amazon’s shipments and external parameters like power availability in each city, the team is developing a plan to gradually electrify Amazon’s entire fleet.

This is just one of many projects the AMZL Research Science team is tackling related to last-mile delivery. Last mile, as the name implies, is the last leg of the journey of a product to a customer’s doorstep. The team develops models to predict shipments per route (SPR) and distribution, which is the average number of packages delivered by a single driver in a given city on a given day (weeks to years in the future). These models help to predict the number and the different sizes of vans the company should purchase to meet the predicted demands.

“With these complex models we develop, we have been influencing the company’s investment in vehicles, Delivery Service Partners, and their drivers,” says Rohit Malshe, a principal research scientist at Amazon.

How to forecast when everything is changing

There are multiple scientific challenges involved in developing these models given the dynamic nature of Amazon’s operations.

“One of these challenges is that our volume keeps growing. In general, as the volume grows, the shipments per route will also increase, but not linearly,” explains Abhilasha Katariya, a senior research scientist on the team. New delivery stations are frequently launched, leading to several changes in the geographical area that each station covers. Stations may incorporate different types of vehicles and modify their operation hours, which also impacts how much they can deliver. Additionally, road networks are subject to alterations as well, impacting driving time.

Left to right, Rohit Malshe, principal research scientist; Abhilasha Katariya, senior research scientist; and Natarajan Gautam, an Amazon Scholar and a professor at Texas A&M University, are all part of the Amazon Logistics Research Science team.
Left to right, Rohit Malshe, principal research scientist; Abhilasha Katariya, senior research scientist; and Natarajan Gautam, an Amazon Scholar and a professor at Texas A&M University, are all part of the Amazon Logistics Research Science team.

The team’s scientists must develop models that can handle the variability and complexity. To do that, they use a bottoms-up approach that starts at the zip code level. “This creates a foundation where any changes in the stations’ jurisdiction can be taken into consideration directly,” says Katariya.

Pure machine-learning approaches are not adequate because the team must frequently make predictions based on new scenarios, for which there is no training data available. To compensate for the lack of training data, the team develops models that combine machine learning and physics-based models that have an optimization component which helps to take into account new variables.

For example, if a large van is added to an Amazon station that previously only worked with small and medium vans, there is no training data to inform the model. “But because the core of the model uses analytical and optimization components, we can still predict the shipments per route for a larger van,” says Katariya.

“If you think about a machine learning model, typically interpolating is very easy. But, in our case, we typically want to extrapolate because we're always getting more volume,” says Natarajan Gautam, an Amazon Scholar and a professor at Texas A&M University. “Using historical data to extrapolate is generally not recommended in machine learning, because you haven’t seen those things in the past.”

This is where the physics-based model comes in handy, although a pure physics-based model also wouldn’t work, notes Gautam, because there are so many simplifying assumptions that need to be made to obtain an analytically tractable model. “We want to get the best of both worlds, in some sense. We all want something that adequately represents what is observed, but we also want to be able to extrapolate when not observed.”

Another strategy the team employs to deal with situations where the parameters are constantly changing is to run the same model over and over again to do a type of course correction. “Just run the model every month, so that all the parameters that are changing are learned by the model, and then you are always getting the latest and greatest picture you should expect. This way you have a good model that handles all types of situations, even the ones where no data exists,” says Malshe.

The science team works very closely with people on the ground, both in station and on the road, to perfect these models. They frequently visit the delivery stations and interview the drivers whenever an opportunity arrives. “We make visits to stations and do ride-alongs so that we stay connected with how the business is evolving,” says Katariya.  

In one of these meetings, Gautam says, station employees said their results were different from what the models were predicting. “We went back to the drawing board, looked at the code and the data they were getting ,and took a deep dive to find what was causing the problem”.

They realized the station started delivering to a new zip code, but it didn’t perform the same way the previous station did. That explained the difference between what the model was observing and the real-life data. Having a close connection with operations allowed them to identify the problem and adjust their model.

Dealing with COVID-19 disruptions

For big decisions like vehicle purchases, the AMZL Research Science team forecasts on a 16-month horizon. However, when the team predicted the number of vans needed for 2020, their model didn’t consider the COVID-19 pandemic. “Suddenly there was so much more package demand that all our forecasts were basically incorrect,” says Malshe.

An Amazon employee loads an electric delivery van inside a delivery station in Los Angeles.
For big decisions like vehicle purchases, the AMZL Research Science team forecasts on a 16-month horizon.
About Amazon

He says, when situations like these arise, the first thing the team does is to upgrade the forecasts to incorporate the additional volume. They also perform scenario analyses to check, for example, if the vehicles that had already been budgeted and procured would serve the purpose. Fortunately, in this case, because these decisions are made so far in advance, the team intentionally overbudgeted to account for uncertainties. “Luckily enough, the previous year, we had spent a lot of money on bigger vehicles, and they were able to absorb the additional package volume. So, when we ran these forecasts, we figured out we were in a good spot to be able to handle such changes,” says Malshe.

“Another risk mitigation lever we applied is to make sure there is enough storage space in the delivery stations,” says Malshe. “We made sure we looked into every possible parameter to optimize for vehicles and their placement in various cities, and their deployment to various Delivery Service Partner companies so that they are utilized to the best of our capabilities.”

‘Many challenges and interesting solutions’

The electrification of Amazon’s fleet presents its own set of challenges. Some of these include how to make sure batteries in the vehicles don’t run out of charge on the road; how to optimize electricity and power consumption; and how to account for extreme weather, long trips and hilly areas. “We will keep learning on all of these items as we go forward, and each year we will come up with more innovations to overcome any barriers,” says Malshe.  

For Malshe, the diversity of the scientists working on the team – which includes people with various backgrounds, industries, educations, and skill sets – is what contributes to its success in tackling these unresolved challenges.

"We have people on our team who are extremely data savvy.  We have team members who know  SQL coding in depth and some are extremely good in Python coding. Other team members have expertise in areas like machine learning, optimization, pure modeling, Monte Carlo simulations and what not," says Malshe, who is himself a chemical engineer with experience in logistics.

 “Usually two to three people are working on every project. It divides and conquers various tasks and ultimately gives everyone an opportunity to do valuable work,” he says.

In addition to the team’s range of expertise, Katariya says another team success factor is its ability to collaborate on a wide range of problems. “Each problem has a different challenge, some have a very simple mathematical solution, but are very heavy on the implementation side, and others may require more complex models from a mathematical perspective, but are easier to implement.”

And there are many more challenges to be tackled. In fact, Gautam says, some of his peers have yet to fully grasp the challenges involved in this field of research.

“A lot of people think of last mile as solving a vehicle routing problem. But we do a lot more than that,” he says. “There are so many challenges and interesting solutions that you just can’t take it off the shelf, you really have to invent as you go along. There are tremendous opportunities to do that here and the range of challenges we get to address is what makes being involved with this team so professionally rewarding.”

The team is currently hiring research and data scientists and is looking for experienced researchers to consider applying.

Related content

GB, Cambridge
Our team builds generative AI solutions that will produce some of the future’s most influential voices in media and art. We develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video, with Amazon Game Studios and Alexa, the ground-breaking service that powers the audio for Echo. Do you want to be part of the team developing the future technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language, Audio and Video technology. As an Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and generative AI models to drive the state of the art in audio (and vocal arts) generation. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. * Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business. * Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
GB, Cambridge
Our team undertakes research together with multiple organizations to advance the state-of-the-art in speech technologies. We not only work on giving Alexa, the ground-breaking service that powers Echo, her voice, but we also develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Senior Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language and Video technology. As a Senior Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech and vocal arts synthesis. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. * Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business. * Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
CN, 11, Beijing
Are you interested in applying your strong quantitative analysis and big data skills to world-changing problems? Are you interested in driving the development of methods, models and systems for strategy planning, transportation and fulfillment network? Are you interested to cooperate with Amazonians around the world? If so, then this is the job for you. Our team, ATE(Analytics Technology and Engineering) is looking for an Applied Scientist to join our growing Science Team in Bangalore (India)/ Beijing(China). We are responsible for creating core analytics tech capabilities, quantative models, platforms development, and data engineering. We develop scalable analytics applications and research models to optimize operations processes. We standardize and optimize data sources and visualization efforts across geographies, build up, and maintain the online business intelligence services and data mart. You will work with other scientists, professional data engineers, business intelligence engineers, and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East. Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of strategic models and automation tools fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in different countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will work on large-scale vehicle routing and scheduling problems under complex operational and physical constraints. You will also identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution of operational plans. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools. Key job responsibilities - Design and develop complex mathematical, simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of vehicle routing, inventory management, network flow, supply chain optimization, demand planning. - Apply theories of mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software. - Translating business questions and concerns into specific analytical questions that can be answered with available data using Statistical and Machine Learning methods. - Prototype models by using modeling and programming languages with efficient data querying and modeling infrastructure. - Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions. - Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds. - Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time. We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN
GB, Cambridge
Our team builds generative AI solutions that will produce some of the future’s most influential voices in media and art. We develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video, with Amazon Game Studios and Alexa, the ground-breaking service that powers the audio for Echo. Do you want to be part of the team developing the future technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language, Audio and Video technology. As an Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and generative AI models to drive the state of the art in audio (and vocal arts) generation. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. * Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business. * Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
US, WA, Bellevue
Do you enjoy solving challenging problems and driving innovations in research? Are you seeking for an environment with a group of motivated and talented scientists like yourself? Do you want to create scalable optimization models and apply machine learning techniques to guide real-world decisions? Do you want to play a key role in the future of Amazon transportation and operations? North America Sort Centers (NASC) are experiencing growth and looking for a skilled, highly motivated Research Scientist in partnership with the Modeling and Optimization (MOP) team. The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network. Key job responsibilities A Research Scientist - provides analytical decision support to Amazon planning teams via applying advanced mathematical and statistical techniques. - collaborates effectively with Amazon internal business customers, and is their trusted partner - is proactive and independent in discovering and resolving business pain-points within a given scope - is able to identify a suitable level of sophistication in resolving the different business needs - is confident in leveraging existing solutions to new problems where appropriate and is independent in designing and implementing new solutions where needed - is aware of the limitations of their proposed solutions and is proactive in communicating them to the business, and advances the application of sciences towards Amazon business problems by bringing new methods, ideas, and practices to the team and scientific community. A day in the life - Your will be developing model-based optimization, simulation, and/or predictive tools to identify and evaluate opportunities to improve customer experience, network speed, cost, and efficiency of capital investment. - You will quantify the improvements resulting from the application of these tools and you will evaluate the trade-offs between potentially competing objectives. - You will develop good communication skills and ability to speak at a level appropriate for the audience, will collaborate effectively with fellow scientists, software development engineers, and product managers, and will deliver business value in a close partnership with many stakeholders from operations, finance, IT, and business leadership. About the team - At the Modeling and Optimization (MOP) team, we use mathematical optimization, algorithm design, statistics, and machine learning to improve decision-making capabilities across WW Operations from first mile to last mile. - We focus on transportation topology, labor and resource planning for fulfillment facilities, routing science, visualization research, data science and development, and process optimization. - We create models to simulate, optimize, and control the fulfillment network with the objective of reducing cost while improving speed and reliability. - We support multiple business lanes, therefore maintain a comprehensive and objective view, coordinating solutions across organizational lines where possible. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, MA, Cambridge
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and models for supervised fine-tuning and reinforcement learning through human feedback; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Cambridge, MA, USA | New York, NY, USA | Sunnyvale, CA, USA
US, MA, North Reading
We are looking for experienced scientists and engineers to explore new ideas, invent new approaches, and develop new solutions in the areas of Controls, Dynamic modeling and System identification. Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Key job responsibilities Applied Scientists take on big unanswered questions and guide development team to state-of-the-art solutions. We want to hear from you if you have deep industry experience in the Mechatronics domain and : * the ability to think big and conceive of new ideas and novel solutions; * the insight to correctly identify those worth exploring; * the hands-on skills to quickly develop proofs-of-concept; * the rigor to conduct careful experimental evaluations; * the discipline to fast-fail when data refutes theory; * and the fortitude to continue exploring until your solution is found We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA
IL, Tel Aviv
Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. We are looking for an Applied Scientist to embark on our journey to build a Prime Video Sports tech team in Israel from ground up. Our team will focus on developing products to allow for personalizing the customers’ experience and providing them real-time insights and revolutionary experiences using Computer Vision (CV) and Machine Learning (ML). You will get a chance to work on greenfield, cutting-edge and large-scale engineering and science projects, and a rare opportunity to be one of the founders of the Israel Prime Video Sports tech team in Israel. Key job responsibilities We are looking for an Applied Scientist with domain expertise in Computer Vision or Recommendation Systems to lead development of new algorithms and E2E solutions. You will be part of a team of applied scientists and software development engineers responsible for research, design, development and deployment of algorithms into production pipelines. As a technologist, you will also drive publications of original work in top-tier conferences in Computer Vision and Machine Learning. You will be expected to deal with ambiguity! We're looking for someone with outstanding analytical abilities and someone comfortable working with cross-functional teams and systems. You must be a self-starter and be able to learn on the go. About the team In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis major like Roland-Garros and English Premium League to list few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere. We are open to hiring candidates to work out of one of the following locations: Tel Aviv, ISR
MX, DIF, Mexico City
Are you a data enthusiast? Does the world’s most complex logistic systems inspire your curiosity? Is your passion to navigate through hundreds of systems, processes, and data sources to solve the puzzles and identify the next big opportunity? Are you a creative big thinker who is passionate about using data and optimization tools to direct decision making and solve complex and large-scale challenges? Do you feel like your skills uniquely qualify you to bridge communication between teams with competing priorities? If so, then this position is for you! We are looking for a motivated individual with strong analytic and communication skills to join the effort in evolving the network we have today into the network we need tomorrow. Amazon’s extensive logistics system is comprised of thousands of fixed infrastructure nodes, with millions of possible connections between them. Billions of packages flow through this network on a yearly basis, making the impact of optimal improvements unparalleled. This magnificent challenge is a terrific opportunity to analyze Amazon’s data and generate actionable recommendations using optimization and simulation. Come build with us! In this role, your main focus will be to perform analysis, synthesize information, identify business opportunities, provide project direction, and communicate business and technical requirements within the team and across stakeholder groups. You consider the needs of day-to-day operations and insist on the standards required to build the network of tomorrow. You will assist in defining trade-offs and quantifying opportunities for a variety of projects. You will learn current processes, build metrics, educate diverse stakeholder groups, assist science groups in initial solution design, and audit all model implementation. A successful candidate in this position will have a background in communicating across significant differences, prioritizing competing requests, and quantifying decisions made. The ideal candidate will have a strong ability to model real world data with high complexity and delivery high quality analysis, data products and optimizations models for strategic decision. They are excited to be part of, and learn from, a large science community and are ready to dig into the details to find insights that direct decisions. The successful candidate will have good communication skills and an ability to speak at a level appropriate for the audience, will collaborate effectively with scientists, product managers and business stakeholders. Key job responsibilities Statistical Models (ML, regression, forecasting, ) Optimization models, AB and hypothesis testing, Bayesian models. Communication skills with both tech and non tech stakeholders. Writting skills, capable to create documents for different types of readers (business, science, tech) to communicate results on analysis, testing. A day in the life We are open to hiring candidates to work out of one of the following locations: Mexico City, DIF, MEX
US, WA, Seattle
We are seeking a Senior Applied Scientist to join our AI Security team and use AI to develop foundational services that make security mechanisms more effective and efficient. As a Senior Applied Scientist, you will be responsible for researching, modeling, designing, and implementing state-of-the-art AI-based security solutions at Amazon scale. You will collaborate with applied scientists, security engineers, software engineers, as well as internal stakeholders and partners to develop innovative technologies to solve some of our hardest security problems, and build paved path solutions that support builder teams across Amazon throughout their software development journey, enabling Amazon businesses to accelerate the pace of innovation to delight our customers. Key job responsibilities • Research and develop accurate and scalable methods to solve foundational security problems. • Lead and partner with applied scientists and engineers to drive modeling and technical design for complex problems. • Build security tooling and paved path solutions that support builder teams throughout their software development journey. About the team ABOUT AmSec: Diverse Experiences: Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security: At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture: In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth: We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance: We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve. We are open to hiring candidates to work out of one of the following locations: Austin, TX, USA | San Francisco, CA, USA | Seattle, WA, USA