This picture is an overhead shot inside an Amazon center, workers can be seen moving amidst hundreds of boxes which sit on conveyor belts and carts, in the upper left foreground, a yellow railing extends into the distance.
When faced with the need to evolve Amazon’s supply chain to meet customer needs, a team of scientists, developers, and other professionals worked together to create an inventory planning system that would help Amazon fulfill its delivery promises.
F4D Studios

The evolution of Amazon’s inventory planning system

How Amazon’s scientists developed a first-of-its-kind multi-echelon system for inventory buying and placement.

For every order placed on the Amazon Store, mathematical models developed by Amazon’s Supply Chain Optimization Technologies organization (SCOT) work behind the scenes to ensure that product inventories are best positioned to fulfill the order. 

Forecasting models developed by SCOT predict the demand for every product. Buying systems determine the right level of product to purchase from different suppliers, while large-scale placement systems determine the optimal location for products across the hundreds of facilities belonging to Amazon’s global fulfillment network.

“With hundreds of millions of products sold across multiple geographies, developing automated models to make inventory planning decisions at Amazon scale is one of the most challenging and rewarding parts of our work,” said Deepak Bhatia, vice president of Supply Chain Optimization Technologies at Amazon.

We made the decision to redesign Amazon’s supply chain systems from the ground up.
Deepak Bhatia

In the first half of the past decade, Amazon transitioned from a largely manual supply chain management system to an automated one. However, when faced with the need to evolve Amazon’s supply chain to meet customer needs, and the introduction of same day delivery services like Prime Now, the team moved to replace that system with a new one that would better help Amazon fulfill delivery promises made to customers.

“As far back as 2016, we were able to see that the automated system we had at the time wouldn’t help us meet the ever-growing expectations of our customers,” Bhatia recalled. “As a result, we made the decision to redesign Amazon’s supply chain systems from the ground up.”

A global company catering to local needs

“In 2016, Amazon’s supply chain network was designed for scenarios where inventory from any fulfillment center could be shipped to any customer to meet a two-day promise,” said Salal Humair, senior principal research scientist at Amazon who has been with the company for seven years.

This design was inadequate for the new world in which Amazon was operating; one shaped by what Humair calls the “globalization-localization imperative.” Amazon’s expansion included an increasing number of international locations — at the time, the company had 175 fulfillment centers serving customers in 185 countries around the world.

“Meeting the needs of our customer base meant that we needed to serve those customers in multiple geographies,” Humair said.

As Amazon continued to expand internationally, the company also launched one-day and same day delivery windows in local regions for services like Amazon Prime and Amazon Prime Now.

“We quickly realized that in addition to serving customers around the globe, we also had to pivot from functioning as a national network to a local one, where we could position inventory close to our customers,” Humair says.

A row of five profile photos shows, left to right, Deepak Bhatia, vice president of Supply Chain Optimization Technologies at Amazon; Salal Humair, senior principal research scientist; Alp Muharremoglu, a senior principal scientist; Jeff Maurer, a vice president; and Yan Xia, principal applied scientist.
Left to right, Deepak Bhatia, vice president of Supply Chain Optimization Technologies at Amazon; Salal Humair, senior principal research scientist; Alp Muharremoglu, a senior principal scientist; Jeff Maurer, a vice president in SCOT; and Yan Xia, principal applied scientist, were among those instrumental in migrating Amazon to the multi-echelon system.

In addition to the ‘globalization-localization imperative,’ the growing complexity of Amazon’s supply chain network further complicated matters. To meet the increased customer demand for a diverse variety of shipping speeds, Amazon’s fulfillment network was expanding to include an increasing number of building types and sizes: from fulfillment centers (for everyday products) and non-sortable fulfillment centers (for larger items), to smaller fulfillment centers catering to same-day orders, and distribution centers that supplied products to downstream fulfillment centers. The network was increasingly becoming layered, and fulfillment centers in one layer (or echelon) were acting as suppliers to other layers.

“We had to reimagine every aspect of our system to account for this increasing number of echelons,” Humair said.

The science behind multi-echelon inventory planning

The sheer scale of Amazons operations posed a significant challenge from a scientific perspective. Amazon Store orders are fulfilled through complex dynamic optimization processes — where a real-time order assignment system can choose to fulfill an order from the optimal fulfillment center that can meet the customer promise. This real-time order assignment makes inventory planning an incredibly complex problem to solve.

Other inventory-related dependencies further complicate matters: the same pool of inventory is frequently used to serve demand for orders with different shipping speeds. Consider a box of diapers: it can be used to fulfill an order for a two-day Prime delivery. It can also be used to ease the life of harried parents who have placed an order on Prime Now, and need diapers for their baby delivered in a two-hour window.

Amazon’s scientists also have to contend with a high degree of uncertainty. Customer demand for products cannot be perfectly predicted even with the most advanced machine learning models. In addition, lead times from vendors are subject to natural variation due to manufacturing capacity, transportation times, weather, etc., adding another layer of uncertainty.

This required building a custom solution, one that relies on sound scientific principles and rigor, and borrowing ideas from academic literature as building blocks, but with ground-breaking in-house invention.
Alp Muharremoglu

Humair notes that the scale of Amazon’s operations, the complexity of the network, and the uncertainties associated with the company’s dynamic ordering system make it impossible to even write down a closed-form objective function for the optimization problem the team was trying to solve.

While multi-echelon inventory optimization is a well-researched field, the bulk of literature focused on single-product models, proposed solutions for much simpler networks, or used greatly simplified assumptions for replenishing inventory.

“There is a large body of academic literature on multi-echelon inventory management, and papers typically focus on one or two main aspects of the problem,” noted Alp Muharremoglu, a senior principal scientist in SCOT who spent 15 years as a faculty member at Columbia University and the University of Texas at Dallas. “Amazon’s scale and complexity meant no existing solution was a perfect fit. This required building a custom solution, one that relies on sound scientific principles and rigor, and borrowing ideas from academic literature as building blocks, but with ground-breaking in-house invention to push the boundaries of academic research. It is a thrill to see multi-echelon inventory theory truly in action in such a large scale and dynamic supply chain.”

As a result, the system developed by SCOT (a project whose roots stretch back to 2016) is a significant break from the past. The heart of the model is a multi-product, multi-fulfillment center, capacity-constrained model for optimizing inventory levels for multiple delivery speeds, under a dynamic fulfillment policy. The framework then uses a Lagrangian-type decomposition framework to control and optimize inventory levels across Amazon’s network in near real-time.

Broadly speaking, decomposition is a mathematical technique that breaks a large, complex problem up into smaller and simpler ones. Each of these problems is then solved in parallel or sequentially. The Lagrangian method of decomposition factors complicated constraints into the solution, while providing a ‘cost’ for violating these constraints. This cost makes the problem easier to solve by providing an upper bound to the maximization problem, which is critical when planning for inventory levels at Amazon’s scale. 

“We computed opportunity costs for storage and flows at every fulfillment center,” Humair said. “Using Lagrangean decomposition, we then used these costs to calculate the related inventory positions at these locations. Crucially, we incorporated a stochastic dynamic fulfillment policy in a scalable optimization model, allowing Amazon to calculate inventory levels not at just one location, but at every layer in our fulfillment network.”

Mobilizing the organization

While creating the new multi-echelon system was an imposing scientific challenge, it also represented a significant organizational accomplishment, one that required collaboration across multiple teams.

“Moving multi-echelon from concept to implementation was one of the most difficult organizational challenges we’ve worked through; we had many potential implementations that looked radically different in terms of model capabilities, interfaces, engineering challenges, and long-term implications for how our teams would interact with each other,” said Jeff Maurer, a SCOT vice president who has been instrumental in rolling out the automation of Amazon’s supply chain and oversaw the roll out of the multi-echelon system.

“This was also a case where there wasn’t a great way to decide between them without building and exploring one or more approaches in production. Ultimately, that’s what we did — we picked the best options we could identify, built them out, learned from them, then repeated that process. We learned things by experimenting with real production implementations that we could never have learned from simplified models or simulations alone, given the complexity of the real-world dynamics of our supply chain. But it was hard on the teams — it wasn’t always obvious that the systems the teams were iterating on were the best path, given the high directional ambiguity.”

Packages moving through a fulfillment center

“Sometimes, the only way to make a massive change is to realize that you have no option but to make that change,” said Yan Xia, principal applied scientist at Amazon. Humair noted that Xia played “a pivotal role” over the four years it took the company to migrate to the new multi-echelon system.

Xia recalled that teams within SCOT were keenly aware of the limitations of the existing system.  However, there was skepticism that the multi-echelon system was the right solution.

“The skepticism was understandable,” Xia said. “It’s one thing to have a big idea. But you also have to be able to present the benefits of your idea in a coherent way.”

Xia gave an example of how he helped convince members from the buying and placement teams about the benefits of the new model.

“One team decides optimal suppliers to source products from, while another team makes decisions on where these products should be placed,” Xia explained. “I was able to show them how the two functions would essentially be unified in the multi-echelon system. Sure, it would change how they worked on a day-to-day basis — but it would do so in a way that made their lives simpler.”

To help ensure that resources were made available for the development of the multi-echelon system, Xia also focused on driving alignment among leaders in SCOT. He developed a simulation based on real-world data. The results clearly demonstrated that the proposed solution for inventory forecasting, buying, and placement would result in a steep decline in shipping costs, which in turn would allow Amazon to keep prices lower for customers.

Teams involved in multi-echelon planning discussions were galvanized after seeing the results of the simulation.

“Everyone bought into the vision,” Xia said. “We began to collaborate in near real-time. If we ran into a problem, we didn’t wait around for a weekly sprint meeting. We just got together in a room, or stood next to a whiteboard and solved it.”

Xia said that this was also when things began to get more complex. 

“An awareness of the complexity of the existing setup began to dawn on us,” says Xia. “We began to realize how every component in the system had multiple dependencies. For example, the buying platforms were tightly integrated with older legacy systems – we now had to factor these dependencies into our solutions.”

Solving a multi-item, multi-echelon with stochastic demand and lead-time and aggregated capacity constraints and differentiated customer service levels. That sort of thing is just unheard of in the academia and the industry.
Deepak Bhatia

The team iterated on the multi-echelon solution in a sequence of three in-production experiments (or labs) that spanned 2018 to 2020. The first lab incorporated components of the new system coupled with the old platform. It was a resounding success in terms of reducing costs, even while fulfilling orders associated with higher shipping speeds. The team moved on to testing the subsequent version of the multi-echelon system in the second lab. 

“That wasn’t nearly as good,” Xia recalled. “Most things didn’t work as expected.”

However, the team was encouraged by leadership to keep going. This wasn’t SCOT’s first attempt at taking on big and ambitious projects. The organization had taken three years to deploy the first automated supply chain management system where they overcame various challenges.

“Sure, the failure of the second lab was demotivating,” Xia says. “But we knew from experience that this failure was only to be expected. It was part of the process.”

The team fixed the bugs, and moved on to testing new features in the third lab. These included critical system capabilities, such the ability to model order cut-off times for deliveries within a particular time window.

The system went live in 2020, and over the past year, the multi-echelon system has had a large and statistically significant impact in positioning products closer to customers.

“On a personal level, I am incredibly proud of our team. Having worked in the area of multi-echelon inventory optimization before I joined Amazon, I have a deep appreciation of how difficult it was,” Bhatia noted. “There is a strong sense of pride for the work the team is doing — such as solving a multi-item, multi-echelon with stochastic demand and lead-time and aggregated capacity constraints and differentiated customer service levels. That sort of thing is just unheard of in academia and industry. This is why I find it gratifying to work as a scientist and a leader at Amazon. It gives me a lot of pride, and none of this could have been achieved without the people and the culture we have.”


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Come join the AWS Marketplace Team in our mission to change the way enterprise software is bought and sold! AWS Marketplace enables software sellers to reach all AWS customers, and it enables software buyers to easily discover, purchase and consume software. Our goals include enriching the platform to support more diverse selection, improving buyer and seller experience, and supporting co-sell opportunities between AWS and our partners. Our vision is to make AWS Marketplace the one stop shop for buying and selling software - we're building the app store for AWS.A day in the lifeIn this role, you’ll be utilizing your creative and critical problem solving skills to drive new projects from ideation to implementation. You will improve and innovate on existing high impact analytical projects. Your outputs will focus on our team’s critical goals and help influence decision makers.About the hiring groupAbout UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.Job responsibilitiesWe are looking for a Data Scientist to join our rapidly growing Seattle team. As a Data Scientist, you are able to use a range of data science methodologies to solve challenging business problems when the solution is unclear. You have a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as Redshift, Sagemaker, Lambda, S3, and EC2 with a variety of skillsets in Tabular ML, NLP, Forecasting, Probabilistic ML and Causal ML. You will bring knowledge in many of these domains along with your own specialties and skillsets.
DE, BE, Berlin
Machine Learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.Machine Learning University’s (MLU) mission is to teach people to apply ML to business and operational challenges. In service of this goal, we are growing our presence outside the US, and are looking to hire in EMEA region. We believe strongly that a practical knowledge of ML can be taught broadly, and is a key skill for many builders to learn. Come join Amazon’s Machine Learning University and help spread knowledge of ML!We have built a team of passionate science educators with the demonstrated ability to explain ML to an audience of technical professionals, enhance curriculum, and collaborate with scientists across the company. You will teach practitioners how to train, tune, and deploy ML models to solve challenges in customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analtyics, and event detection among others. Practical experience with machine learning is key to ensure content is up-to-date, practical and engaging. This is a full-time role where you will constantly be challenged to learn and be curious about ML.As a Sr. MLU Applied Scientist on our team, you would have the following responsibilities:· Teach customers directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Develop open-source curriculum designed to provide a practical knowledge of ML. This includes lectures, videos, demos, and coding notebook assignments (find an article about our largest release here: https://www.amazon.science/latest-news/machine-learning-course-free-online-from-amazon-machine-learning-university)· Collaborate with scientists across Amazon to continuously understand and integrate advances in ML into the curriculum· Help drive high-level curriculum decisions to ensure alignment with students’ needs and the state-of-the-art domains of advancement in ML· Consult with customers on model development and deployment best practices by using computer science fundamentals· Engage in the interview process and otherwise develop, grow, and mentor junior scientistsThis team is comprised of Data Scientists, Applied Scientists and Instructional Designers to create first-in-class courses and learning assets in practical ML. The role may require travel up to 40% of the time (pending safety protocols and travel restrictions). We are currently recruiting for talented individuals for this role in the following cities: London, Cambridge, Tuebingen, Dublin, Amsterdam, Berlin, Paris, or Gdansk. Let’s bring ML to everyone and demystify and democratize technology together!Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfilment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We Hire and Develop the best so you can expect support in advancing your career ambitions and projects which will help you grow and develop your skills.
DE, BE, Berlin
Machine Learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.Machine Learning University’s (MLU) mission is to teach people to apply ML to business and operational challenges. In service of this goal, we are growing our presence outside the US, and are looking to hire in EMEA region. We believe strongly that a practical knowledge of ML can be taught broadly, and is a key skill for many builders to learn. Come join Amazon’s MLU and help spread knowledge of ML!We have built a team of passionate science educators with the demonstrated ability to explain ML to an audience of technical professionals, enhance curriculum, and collaborate with scientists across the company. You will teach practitioners how to train, tune, and deploy ML models to solve challenges in customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analtyics, and event detection among others. Practical experience with machine learning is key to ensure content is up-to-date, practical and engaging. This is a full-time role where you will constantly be challenged to learn and be curious about ML.As a MLU Applied Scientist on our team, you would have the following responsibilities:· Teach customers directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Develop open-source curriculum designed to provide a practical knowledge of ML. This includes lectures, videos, demos, and coding notebook assignments (find an article about our largest release here: https://www.amazon.science/latest-news/machine-learning-course-free-online-from-amazon-machine-learning-university)· Collaborate with scientists across Amazon to continuously understand and integrate advances in ML into the curriculum· Help drive high-level curriculum decisions to ensure alignment with students’ needs and the state-of-the-art domains of advancement in ML· Consult with customers on model development and deployment best practices by using computer science fundamentalsThis team is comprised of Data Scientists, Applied Scientists and Instructional Designers to create first-in-class courses and learning assets in practical ML. The role may require travel up to 40% of the time (pending safety protocols and travel restrictions). We are currently recruiting for talented individuals for this role in the following cities: London, Cambridge, Tuebingen, Dublin, Amsterdam, Berlin, Paris, or Gdansk. Let’s bring ML to everyone and demystify and democratize technology together!Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfilment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We Hire and Develop the best so you can expect support in advancing your career ambitions and projects which will help you grow and develop your skills.
US, WA, Seattle
Job summaryThe AWS Cryptography Group is looking for an Applied Scientist with knowledge of cryptographic computing technologies such as privacy preserving machine learning, fully and partially homomorphic encryption, secure multiparty computation, private information retrieval, and related technologies. You will use this knowledge to conceptualize how this technology can be integrated into internal infrastructure and public AWS services, develop prototypes, and provide customers with world-class security. The ideal candidate will have a strong understanding of cryptography, the ability to prototype solutions, and a passion to realize these technologies in AWS products and services. We encourage research and publication of results that apply to our customers most complex initiatives.We are seeking a candidate who is innovative, looking for new ideas everywhere. They should think big, and have bold ideas for new ways to serve customers.About UsAWS has the most services and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. AWS Platform is the glue that holds the AWS ecosystem together. Whether its Identity features such as access management and sign on, cryptography, console, builder & developer tools, and even projects like automating all of our contractual billing systems, AWS Platform is always innovating with the customer in mind. The AWS Platform team sustains over 750 million transactions per second.A Culture of InclusionHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceWe put a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We provide both the opportunity to be mentored by seasoned leaders and to mentor junior researchers. We care about your career growth and strive to give you challenging responsibilities that will help you develop into a better-rounded applied scientist and give you the opportunity to advance your career to the next level.Learn more about Amazon on our Day 1 Blog: https://blog.aboutamazon.com/
US, VA, Arlington
Job summaryThe Group:The Database Services group provides rapidly growing, industry acclaimed cloud services in areas of big data platforms, data warehouses, analytics, and operational databases. The group is at the forefront of innovation in these areas producing world-class cloud services, which while large profitable businesses, are also in their infancy. They represent a large fraction of the AWS business, and continue to accelerate.The Team:Lake Formation is our fully managed service to simplify building and securing data lakes. It features Blueprints to ingest data from commons sources, ML transforms to clean and de-duplicate data and a unified security model with fine-grained permissions (at the database, table, and column level) that are applicable across a wide range of AWS analytic and ML services (Redshift Spectrum, Glue, EMR Spark, QuickSight).The Lake Formation team is looking for an Applied Scientist with experience in building secure, scalable solutions that delight customers. You will apply your knowledge to propose solutions, create software prototypes, and productize prototypes into production systems using software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. You have strong verbal and written communication skills, are self-driven and deliver high quality results in a fast-paced environment.Technical Responsibilities:· Interact with various teams to develop an understanding of their security and safety requirements.· Apply the acquired knowledge to build tools find problems, or show the absence of security/safety problems.· Implement these tools through the use of SAT, SMT, BDDs, and various concepts from programming languages, theorem proving, formal verification and constraint solving.· Perform analysis of the customer systems using tools developed in-house or externally provided· Create software prototypes to verify and validate the devised solutions methodologies; integrate the prototypes into production systems using standard software development tools and methodologies.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
Job summaryAre you passionate about big data and machine learning? Do you enjoy solving complex analytical problems, build predictive analytics models and develop insights and recommendations at web scale? Do you want to make an impact in Amazon’s multi-billion-dollar Messaging business? Amazon’s Outbound Communication Services, OCS, owns foundational systems that power all of Amazon's customer-facing communication on Email, SMS, Push channels, and other emerging messaging applications. In 2020, we sent over 100 billion messages to our global customers on these channels! OCS builds self-governing and message-optimizing engines that leverage integrated Machine Learning, massive data processing and adaptive algorithms to deliver the best messages to Amazon’s customers, over the best channel, and at the right time. Our systems ensure best-in-class engagement experience, which spans transactional and marketing communications. We're building a top-notch team of engineers, applied scientists, data engineers, and engineering leaders. The problems we face are complex and interesting including information engineering, data mining of Big Data sets, and governing real-time messages at scale. We build large scale, distributed systems using multiple AWS services that we designed from the ground up.We are looking for a strong Applied Scientist to work backwards from customers, create models and develop insights, deliver impact, and drive growth of the OCS worldwide. As a Senior Applied Scientist on our team, you will be working with business stakeholders, product/program managers, developers and executives to deeply understand customer problems and priorities. You will form hypotheses, analyze the corpus of Outbound and Amazon data using statistical methods, build predictive models, generate recommendations to address a range of problems to optimize the value of the messages we send to our customers. These span developing insights on customer engagement trends, building models to inform message rates, channel selection, and defining segmentation for marketing, etc. Your expertise will enable us to enable new customer experiences while maintaining best-in-class customer experience. You will be comfortable with big data systems, intimately familiar with advanced machine learning and statistical methods, and experienced in applying these to solve business problems. You will have a strong bias towards customer obsession and delivering results while dealing with ambiguity in fast-paced dynamic environments.Roles and Responsibilities· Work with business teams, product/program managers, engineers and leadership to identify and prioritize customer and business problems· Translate these problems into specific analytical questions and form hypotheses that can be answered with available data using statistical methods or identify additional data needed in the master datasets to fill any gaps· Perform hand-on data analyses and modeling with huge datasets to develop insights and recommendations to inform decisions across the program· Design and run A/B experiments to validate the hypotheses and evaluate the impact of your optimizations and communicate your results to various stakeholders· Collaborate with engineers and product managers to build scalable solutions and new capabilities· Help on projects of high visibility across the organization and deliver business impact
US, MA, Cambridge
Job summaryThe Alexa Artificial Intelligence (AI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading Speech and Language technology. Our mission is to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers.Key job responsibilitiesAs an Applied Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.
US, WA, Bellevue
Job summaryAmazon’s Modeling and Optimization Team is looking for a senior science leader (Principal Research Scientist) to drive the development and optimization of the most complex transportation and fulfillment network in the world.The team is responsible for optimizing the global transportation and fulfillment network for Amazon.com and ensuring that the company is able to deliver our customers’ products to them as quickly, accurately, and cost effectively as possible. We design the network that delivers packages from fulfillment centers (FCs) to end customers, through both Amazon’s internal network as well as external partners, using ground and air methods. Optimizing these package flows requires designing the structure and operating parameters of the network, optimizing operations such as linehaul movements and sortation scheduling, and making the right capital investments in technology, buildings and equipment. It also requires partnering with peer research and product teams that drive inventory placement, selection, and forecasting, and incorporating information and algorithmic interfaces with those systems.We are seeking an experienced multi-disciplinary science leader, to lead the science development on projects in both developing algorithmic tools and driving long-term network strategy. In this role, you will work with and advise other research scientists in the team, develop your own scientific approaches, and partner with software and product teams as they deploy algorithmic solutions to our stakeholders. You will also partner with business leaders on projects that evaluate long-term strategic network choices, and present strategy papers to our senior-most leaders to drive long-term impact.To accomplish this, we expect you to have a strong research background in at least one of the following disciplines: operations research, computer science, operations management, statistics, or applied mathematics. You should also have business domain knowledge in transportation and distribution operations, along with some knowledge of inventory management theory and practice. You should have a strong publication record that demonstrates technical depth as well as consideration of practical aspects in your work. Experience partnering with companies on high-stakes R&D or consulting is a plus, but not a necessity.
US, VA, Arlington
Job summaryInterested in machine learning and AI? Can you envision a future where technology is driven primarily by smarter machines?The mission of AWS AI is to make machine learning easy, fast, and universal across all of our customers. Our world class platform provides the services that runs 85% of all on-cloud machine learning through performance optimizations, machine learning tools, and SDKs to democratize machine learning. Our customers include scientists, data analysts, and ML engineers all building and deploying models through either SageMaker or self-managed instances on EC2/EKS.The AWS Machine Learning Services group in Amazon AI is seeking applicants for an applied scientist position in the areas of fairness, bias, explainability, privacy, and model understanding in ML. As an applied scientist in AWS AI, you will be driving many scientific breakthroughs to help our customers understand and trust ML-based outcomes, and also working with software engineers to deploy these solutions to AWS customers. You will be working on the ML platforms and services that power over 85% of machine learning in the cloud, and help make machine learning fast, accessible, universal, and trustworthy for all our customers.We look for candidates with PhD in a relevant technical field (such as Algorithms, Machine Learning, AI, and Mathematics), preferably with interest / experience in explainability, fairness, privacy, and model understanding. We also encourage graduating PhD students as well as recent PhD graduates to apply. You will be working closely with software engineers so experience with cloud systems is a benefit. Our customers are deeply technical and the solutions we build for them are strongly coupled to technical feasibility. You must be able to thrive and succeed in an entrepreneurial environment, and not be hindered by ambiguity or competing priorities. This means you are not only able to develop and drive high-level strategic initiatives, but can also roll up your sleeves, dig in and get the job done. Ownership, high judgment, negotiation skills, ability to influence, analytical talent and leadership are essential to success in this role.For more information about AWS AI team, please see https://aws.amazon.com/machine-learning and https://aws.amazon.com/sagemaker.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
The Team: Amazon Go is a new kind of store with no lines and no checkout—you just grab and go! Customers simply use the Amazon Go app to enter the store, take what they want from our selection of fresh, delicious meals and grocery essentials, and go!Our checkout-free shopping experience is made possible by our Just Walk Out Technology, which automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt. Check it out at amazon.com/go. Designed and custom-built by Amazonians, our Just Walk Out Technology uses a variety of technologies including computer vision, sensor fusion, and advanced machine learning. Innovation is part of our DNA! Our goal is to be Earths’ most customer centric company and we are just getting started. We need people who want to join an ambitious program that continues to push the state of the art in computer vision, machine learning, distributed systems and hardware design.The Role: Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems.As a Computer Vision Research Scientist, you will help solve a variety of technical challenges and mentor other engineers. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved at scale before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people.
US, WA, Seattle
Job summaryJoin us in the evolution of Amazon’s consumer business!The Brand Program and Selling Partner Development organization is the growth and development engine for our Store. Our business grows when our Selling Partners (SPs) businesses’ grow. We aspire to make Amazon the best place for Selling Partners to sell and measure our progress towards this goal through a combination of metrics. We make it easy and predictable for Selling Partners to achieve a great Customer Experience. We believe all motivated Selling Partners that provide a great Customer Experience should be able to grow their business on Amazon either guided by unambiguous self-service tools or by a named Account Manager (AM) working from the same set of recommendations and insights. By ensuring that the same set of recommendations and insights are used, we democratize growth, increase predictability and build trust with our SPs. Our front line AMs drive a virtuous flywheel of learning, experimentation, development and scale that accelerates our store and improves the Customer Experience.We have numerous, durable, deep, technical and interesting problems for Sr. Data Scientist to own. We are looking for a Sr. Data Scientist to design and implement deep tools, insights, metrics and models (inclusive of AI) to lead our systems and tools. You will work with a range of strategic functions within the consumer organization. Our broader org. certainly embodies a start-up mentality. You will work closely with data engineers, software developers, product managers to come up with solution end-to-end with cutting edge machine learning and AI technologies.
US, CA, Sunnyvale
MULTIPLE POSITIONS AVAILABLEEntity: Amazon.com Services LLC, an Amazon.com CompanyTitle: Applied Scientist IILocation: Sunnyvale, CAPosition Responsibilities:Use expertise in physical sciences as well as theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices. Develop innovative analyses and tests to study viability of new materials, designs and processes. Work closely with engineering teams to drive validation, optimization and implementation of hardware designs and software algorithmic solutions to improve product and reduce customer risks. Establish scalable, efficient, automated processes to handle large-scale design and data analysis. Conduct research on user behavior, materials and analysis techniques. Track general business activity including device health in field and provide clear, compelling reports to management on a regular basis. Develop and implement guidelines to continually optimize design processes. Perform structural tests on materials and components used in consumer electronics systems to drive design and materials recommendations in consumer electronics systems. Implement experimental engineering techniques to validate and address structural design risks in consumer electronic systems.Amazon.com is an Equal Opportunity-Affirmative Action Employer - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. #0000
US, TX, Austin
Job summaryAmazon's CloudTune group is looking for an experienced Applied Scientist to join our forecasting team. The team develops large-scale scale models to inform team-level budget allocations and procurement/allocation of compute capacity for Amazon businesses during new product launches, high velocity events and non-peak periods. This role will be contributing to a managed service that uses historical data and business signals to deliver time series forecasting for specialized use cases. The service combines a variety of distinct forecasting models, including neural networks, to produce highly accurate forecasts.As a scientist in the CloudTune team you'll also partner with technology and business teams to build new services that surprise and delight our customers. We develop sophisticated algorithms that involve learning from large amounts of past data. These forecasts are used to determine the level of investment in capital expenditures, promotional activity, engineering efficiency projects and determining financial performance.You will work on mathematical problems with a high level of ambiguity. You will analyze and process large amounts of data, develop new algorithms and improve existing approaches based on statistical models, machine learning algorithms and big data solutions to automatically scale Amazon’s compute infrastructure, optimizing the balance between availability risk and cost efficiency for all of Amazon businesses.We are looking for top scientists capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. We're an agile team with significant impact. If you can think big and want to be a part of a fast moving team breaking new ground at Amazon.com, and you meet the qualifications, we would like to speak with you!Key job responsibilities· Process and analyze large data sets, mining additional data sources as needed· Analyze compute scaling metrics to identify business drivers that influence infrastructure expenditures· Build mathematical models to represent demand forecasting at various levels.· Prototype these models by using high-level modeling languages such as R or in software languages such as Python. A software team will be working with you to transform prototypes into production.· Create, enhance, and maintain technical documentation, and present to other scientists and business leaders.
US, CA, Sunnyvale
Job summaryAlexa is the groundbreaking voice service that powers Echo and other devices designed around your voice. Our team is creating the science and technology behind Alexa. We’re working hard, having fun, and making history. Come join our team! You will have an enormous opportunity to impact the customer experience, design, architecture, and implementation of a cutting edge product used every day by people you know.We’re looking for a passionate, talented, and inventive scientist to help build industry-leading conversational technologies that customers love. As an Applied Scientist, you will work with talented peers to develop novel machine learning algorithms and modeling techniques to advance the state of the art in speech and audio processing. Your work will directly impact our customers in the form of novel products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. You will mentor junior scientists, create and drive new initiatives.