Amazon Science Forecasting Algorithm.png

The history of Amazon’s forecasting algorithm

The story of a decade-plus long journey toward a unified forecasting model.

When a customer visits Amazon, there is an almost inherent expectation that the item they are searching for will be in stock. And that expectation is understandable — Amazon sells more than 400 million products in over 185 countries.

However, the sheer volume of products makes it cost-prohibitive to maintain surplus inventory levels for every product.

Related content
How Amazon’s Supply Chain Optimization Technologies team has evolved over time to meet a challenge of staggering complexity.

Historical patterns can be leveraged to make decisions on inventory levels for products with predictable consumption patterns — think household staples like laundry detergent or trash bags. However, most products exhibit a variability in demand due to factors that are beyond Amazon’s control.

Take the example of a book like Michelle Obama’s ‘Becoming’, or the recent proliferation of sweatsuits, which emerged as both comfortable and fashion-forward clothing option during 2020. It’s difficult to account for the steep spike in sales caused by a publicity tour featuring Oprah Winfrey, and nearly impossible to foresee the effect COVID-19 would have on, among other things, stay-at-home clothing trends.

Today, Amazon’s forecasting team has drawn on advances in fields like deep learning, image recognition and natural language processing to develop a forecasting model that makes accurate decisions across diverse product categories. Arriving at this unified forecasting model hasn’t been the result of one “eureka” moment. Rather, it has been a decade-plus long journey.

Hands-off-the-wheel automation: Amazon’s supply chain optimization

“When we started the forecasting team at Amazon, we had ten people and no scientists,” says Ping Xu, forecasting science director within Amazon’s Supply Chain Optimization Technologies (SCOT) organization. “Today, we have close to 200 people on our team. The focus on scientific and technological innovation has been key in allowing us to draw an accurate estimate of the immense variability in future demand, and make sure that customers are able to fulfill their shopping needs on Amazon.”

In the beginning: A patchwork of models

Kari Torkkola, senior principal research scientist, has played a key role in driving the evolution of Amazon’s forecasting systems in his 12 years at the company.

“When I joined Amazon, the company relied on traditional time series models for forecasting,” says Torkkola.

Clockwise from top left, Ping Xu, forecasting science director; Kari Torkkola, senior principal research scientist; Dhruv Madeka, principal applied scientist; and Ruofeng Wen, senior applied scientist
Clockwise from top left, Ping Xu, forecasting science director; Kari Torkkola, senior principal research scientist; Dhruv Madeka, principal applied scientist; and Ruofeng Wen, senior applied scientist

Time series forecasting is a statistical technique that uses historical values and associated patterns to predict future activity. In 2008, Amazon’s forecasting system used standard textbook time series forecasting methods to make predictions.

The system produced accurate forecasts in scenarios where the time series was predictable and stationary. However, it was unable to produce accurate forecasts for situations such as new products that had no prior history or products with highly seasonal sale patterns. Amazon’s forecasting teams had to develop new methods to account for each of these scenarios.

The system was incredibly hard to maintain. It gradually became clear that we needed to work towards developing a unified forecasting model.
Kari Torkkola

So they set about developing an add-on component to model seasonal patterns in products such as winter jackets. Another specialized component solved for the effects of price elasticity, where products see spikes in demand due to price drops, while yet another component called Distribution Engine modeled past errors to produce estimates of forecast distributions on top of point forecasts.

“There were multiple components, all of which needed our attention,” says Torkkola. “The system was incredibly hard to maintain. It gradually became clear that we needed to work towards developing a unified forecasting model.”

Enter the random forest

If the number of components made maintaining the forecasting system laborious, routing special forecasting cases or even product groups to specialized models, which involved encoding expert knowledge — complicated matters even further.

Then Torkkola had a deceptively simple insight as he began working toward a unified forecasting model. “There are products across multiple categories that behave the same way,” he said.

Related content
Danielle Maddix Robinson's mathematics background helps inform robust models that can predict everything from retail demand to epidemiology.

For example, there is clear delineation between new products and products with an established history. The forecast for a new video game or laptop can be generated, in part, from how similar products behaved when they had launched in the past.

Torkkola extracted a set of features from information such as demand, sales, product category, and page views. He used these features to train a random forest model. Random forests are commonly used machine learning algorithms that comprise  a number of decision trees. The outputs of the decision trees are then bundled together to provide a more stable and accurate prediction.

“By pooling everything together in one model, we gained statistical strength across multiple categories,” Torkkola says.

At the time, Amazon’s base forecasting system produced point forecasts to predict future demand — a single number that conveys information about the future demand. However, full forecast distributions or a set of quantiles of the distribution are necessary when it comes to make informed forecasting decisions on inventory levels. The Distribution Engine, which was another add-on to the base system, was producing poorly calibrated distributions.

Related content
Learning the complete quantile function, which maps probabilities to variable values, rather than building separate models for each quantile level, enables better optimization of resource trade-offs.

Torkkola wrote an initial implementation of the random forest approach to output quantiles of forecast distributions. This was rewritten as a new incarnation called Sparse Quantile Random Forest (SQRF). That implementation allowed a single forecasting system to make forecasts for different product lines where each may have had different features present, thus each of those features seem very “sparse”. SQRF could also scale to millions of products, and represented a step change for Amazon to produce forecasts at scale.

However, the system suffered from a serious drawback. It still required the team to manually engineer features for the model — in other words, the system needed humans to define the input variables that would provide the best possible output.

That was all set to change in 2013, when the field of deep learning went into overdrive.

Deep learning produces the unified model

“In 2013, there was a lot of excitement in the machine learning community around deep learning,” Torkkola says. “There were significant advances in the field of image recognition. In addition, tensor frameworks such as THEANO developed by the University of Montreal were allowing developers to build deep learning models on the fly. Currently popular frameworks such as TensorFlow were not yet available.”

Neural networks held a tantalizing prospect for Amazon’s forecasting team. In theory, neural networks could do away with the need to manually engineer features. The network could ingest raw data and learn the most relevant implicit features needed to produce a forecast without human input.

With the help of interns hired over the summers of 2014 and 2015, Torkkola experimented with both feed forward and recurrent neural networks (RNNs). In feed forward networks, the connections between nodes do not form a cycle; the opposite is true with RNNs. The team began by developing a RNN to produce a point forecast. Over the next summer, another intern developed a model to produce a distribution forecast. However, these early iterations did not outperform SQRF, the existing production system.

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

Amazon’s forecasting team went back to the drawing board and had another insight, one that would prove crucial in the journey towards developing a unified forecasting model.

“We trained the network on minimizing quantile loss over multiple forecast horizons,” Torkkola says. Quantile loss is among the most important metrics used in forecasting systems. It is appropriate when under- and over-prediction errors have different costs, such as in inventory buying.

“When you train a system on the same metric that you are interested in evaluating, the system performs better,” Torkkola says. The new feed forward network delivered a significant improvement in forecasting relative to SQRF.

This was the breakthrough that the team had been working towards: the team could finally start retiring the plethora of old models and utilize a unified forecasting model that would produce accurate forecasts for multiple scenarios, forecasts, and categories. The result was a 15-fold improvement in forecast accuracy and great simplification of the entire system.

At last, no feature engineering!

While the feed forward network had delivered an impressive improvement in performance, the system still continued using the same hand engineered features SQRF had used. "There was no way to tell how far those features were from optimal," Ruofeng Wen, senior applied scientist who formerly worked as a forecasting scientist and joined the project in 2016, pointed out. “Some were redundant, and some were useless.”

Related content
Method uses metric learning to determine whether images depict the same product.

The team set out to develop a model that would remove the need to manually engineer domain-specific features, thus being applicable to any general Forecasting problem. The breakthrough approach, known as MQ-RNN/CNN, was published in a 2018 paper titled "A Multi-Horizon Quantile Recurrent Forecaster". It built off the recent advances made in recurrent networks (RNN) and convolutional networks (CNNs).

CNNs are frequently used in image recognition due to their ability to scan an image, determine the saliency of various parts of that image, and make decisions about the relative importance of those facets. RNNs are usually used in a different domain, parsing semantics and sentiments from texts. Crucially, both RNNs/CNNs are able to extract the most relevant features without manual engineering. “Afterall, forecasting is based on past sequential patterns,” Wen said, “and RNNs/CNNs are pretty good at capturing them.”

Leveraging the new general approach allowed Amazon to forecast the demand of any fast-moving products by a single model structure. This out-performed a dozen of legacy systems designed for difference product lines, since the model was smart enough to learn business-specific demand patterns all by itself. However, for a system to make accurate predictions about the future, it has to have a detailed understanding of the errors it has made in the past. However, the architecture of Multi-Horizon Quantile Recurrent Forecaster had few mechanisms that would enable the model to ingest knowledge about past errors.

Amazon’s forecasting team worked through this limitation by turning to the latest advances in natural language processing (NLP).

Leaning on natural language processing

Dhruv Madeka, a principal applied scientist who had conducted innovative work in developing election forecasting systems at Bloomberg, was among the scientists who had joined Amazon’s forecasting team in 2017.

“Sentences are a sequence of words,” Madeka says. “The attention mechanisms in many NLP models look at a sequence of words, and determine which other parts of the sentence are important for a given context and task. By incorporating these context-aware mechanisms, we now had a way to make our forecasting system pay attention to its history, and gain an understanding of the errors it had made in the past.”

Amazon’s forecasting team honed in on the transformer architectures that were shaking up the world of NLP. Their new approach, which used decoder-encoder attention mechanisms for context-alignment, was outlined in the paper "MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention" published in December 2020. The decoder-encoder attention mechanisms meant that the system could study its own history and improve forecasting accuracy and decrease the volatility of the forecast.

With MQ Transformer, Amazon now has a unified forecasting model able to make even more accurate predictions across the company’s vast catalog of products.

Today, the team is developing deep reinforcement learning models that will enable Amazon to ensure that the accuracy improvements in forecasts translate directly into cost savings, resulting in lower costs for customers. To design a system that optimizes directly for savings — as opposed to inventory levels — the forecasting team is drawing on cutting-edge research from fields such as deep reinforcement learning.

“Amazon is an exceptional place for a scientist because of the focus on innovation grounded on making a real impact,” says Xu. “Thinking big is more than having a bold vision. It involves planting seeds, growing it continuously by failing fast, and doubling down on scaling once the evidence of success becomes apparent.”

View from space of a connected network around planet Earth representing the Internet of Things.
Sign up for our newsletter

Related content

US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. 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. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. Key job responsibilities • Develop automated laboratory workflows. • Perform data QC, document results, and communicate to stakeholders. • Maintain updated understanding and knowledge of methods. • Identify and escalate equipment malfunctions; troubleshoot common errors. • Participate in the updating of protocols and database to accurately reflect the current practices. • Maintain equipment and instruments in good operating condition • Adapt to unexpected schedule changes and respond to emergency situations, as needed. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Are you excited about developing generative AI and foundation models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for scientists, engineers and program managers for a variety of roles. The Amazon Robotics software team is seeking a Applied Scientist to focus on large vision and manipulation machine learning models. This includes building multi-viewpoint and time-series computer vision systems. It includes using machine learning to drive hardware movement. It includes building large-scale models using data from many different tasks and scenes. This work spans from basic research such as cross domain training, to experimenting on prototype in the lab, to running wide-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery – Proving/dis-proving strategies in offline data or in the lab * Production studies - Insights from production data or ad-hoc experimentation. About the team This team invents and runs robots focused on grasping and packing items. These are typically 6-dof style robotic arms. Our work ranges from the long-term-research on basic science to deploying/supporting large production fleets handling billions of items per year. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, VA, Arlington
Amazon launched the Generative AI (GenAI) Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate enterprise innovation and success with Generative AI (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). Customers such as Highspot, Lonely Planet, Ryanair, and Twilio are engaging with the GAI Innovation Center to explore developing generative solutions. GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As a data scientist at GAIIC, you are proficient in designing and developing advanced Generative AI based solutions to solve diverse customer problems. You will be working with terabytes of text, images, and other types of data to solve real-world problems through Gen AI. You will be working closely with account teams and ML strategists to define the use case, and with other scientists and ML engineers on the team to design experiments, and find new ways to deliver value to the customer. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners. This position requires that the candidate selected be a US Citizen and currently possess and maintain an active Top Secret security clearance. About the team Work/Life Balance Our 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 Growth Our 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. 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. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, VA, Arlington
Amazon’s mission is to be the most customer centric company in the world. The Workforce Staffing (WFS) organization is on the front line of that mission by hiring the hourly fulfillment associates who make that mission a reality. To drive the necessary growth and continued scale of Amazon’s associate needs within a constrained employment environment, Amazon has created the Workforce Intelligence (WFI) team. This team will (re)invent how Amazon attracts, communicates with, and ultimately hires its hourly associates. This team owns multi-layered research and program implementation to drive deep learning, process improvements, and strategic recommendations to global leadership. Are you passionate about data? Do you enjoy questioning the status quo? Do complex and difficult challenges excite you? If yes, this may be the team for you. The Data Scientist will be responsible for creating cutting edge algorithms, predictive and prescriptive models as well as required data models to facilitate WFS at-scale warehouse associate hiring. This role acts as an internal consultant to the marketing, biz ops and candidate experience teams covering responsibilities such as at-scale hiring process improvement, analyzing large scale candidate/associate data and being strategic to providing best candidate hiring experience to WFS warehouse associate candidates. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, CA, Sunnyvale
At Amazon Fashion, we are obsessed with making Amazon Fashion the most loved fashion destinations globally. We're searching for Computer Vision pioneers who are passionate about technology, innovation, and customer experience, and who are enthusiastic about making a lasting impact on the industry. You'll be working with talented scientists, engineers, and product managers to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey and change the world of eCommerce forever Key job responsibilities As a Applied Scientist, you will be at the forefront to define, own and drive the science that span multiple machine learning models and enabling multiple product/engineering teams and organizations. You will partner with product management and technical leadership to identify opportunities to innovate customer facing experiences. You will identify new areas of investment and work to align product roadmaps to deliver on these opportunities. As a science leader, you will not only develop unique scientific solutions, but more importantly influence strategy and outcomes across different Amazon organizations such as Search, Personalization and more. This role is inherently cross-functional and requires a strong ability to communicate, influence and earn the trust of software engineers, technical and business leadership. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
US, CA, Sunnyvale
Are you passionate about solving unique customer-facing problem in the Amazon scale? Are you excited by developing and productizing machine learning, deep learning algorithms and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The mission of Fit science team as part of Fashion Tech is to innovate and develop scalable ML solutions to provide personalized fit and size recommendation when Amazon Fashion customers evaluate apparels or shoes online. The team is hiring Applied Scientist who has a solid background in applied Machine Learning and a proven record of solving customer-facing problems via scalable ML solutions, and is motivated to grow professionally as an ML scientist. Key job responsibilities - Tackle ambiguous problems in Machine Learning and drive full life-cycle Machine Learning projects. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production. - Run A/B experiments, gather data, and perform statistical tests. - Establish scalable, efficient, automated processes for large-scale data mining, machine-learning model development, model validation and serving. - Work closely with software engineers and product managers to assist in productizing your ML models. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
US, WA, Bellevue
Have you ever wondered how Amazon predicts when your order will arrive and how we ensure that it actually arrives on at the promised date/time? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's logistics network including our planes, trucks, and vans sound exciting to you? If so, then we want to talk with you! The Network Planning and Fulfillment Execution team owns and operates OR/ML and simulation systems that continually optimize the distribution of tens of millions of products across Amazon’s warehouses in the most cost-effective manner, utilizing large scale optimization techniques and distributed computing in trying to reduce overall transportation costs while improving the customer experience. We are focused on saving hundreds of millions of dollars using big data technologies, cutting edge science, machine learning, and scalable distributed software on the cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing and supply. We’re looking for a passionate, results-oriented, and inventive Research Scientist who can create and improve OR/ML models for our outbound transportation planning systems. In addition, you will be working on design, development and evaluation of highly innovative OR and ML models for solving complex business problems in the area of outbound transportation planning systems. More specifically, you will be developing a Mathematical Optimization model towards short term Origin-Destination flows that are inventory aware and adhere to facility capacities given destination demand. This will also require you to build machine learning models to predict inventory N weeks out (N<13 Weeks) and ML models to calibrate inventory bounds and math model errors. You will work closely with our product managers and software engineers to disambiguate complex supply chain problems and create ML solutions to solve those problems at scale. You will directly impact our direct customers, and even play with big data and incredible scale in the background. Watch http://bit.ly/amazon-scot to get the big picture. Key job responsibilities As part of your daily work you will: * Design, development and evaluation of highly innovative OR/ML models for solving complex business problems. * Analyze and extract relevant information from large amounts of data to help automate and optimize key processes. * Research and apply the latest ML techniques and best practices from both academia and industry. * Think about customers and how to improve the customer delivery experience. * Use and analytical techniques to create scalable solutions for business problems. * Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. * Technically lead and mentor other scientists in team. * Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. A day in the life This is a great role for someone who likes to learn new things. You will have the opportunity to learn all about how Amazon plans for and executes within it's logistics network including Fulfillment Centers, Sort Centers, Delivery Stations, and more. In this role, you will be a design and develop Optimization and Machine Learning models with significant scope, impact, and high visibility. Your solutions will impact business segments worth many-billions-of-dollars and geographies spanning multiple countries and markets. From day one, you will be working with bar raising scientists, engineers, and designers. You will also collaborate with the broader science community in Amazon to broaden the horizon of your work. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team Network Planning and Fulfillment Execution Science team contains a group of scientists with different technical backgrounds including Machine Learning and Operations Research, who will collaborate closely with you on your projects. Our team directly supports multiple functional areas across Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We tackle some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide and at a scale that is unique to Amazon. We often seek the opportunity of applying hybrid techniques in the space of Operations Research and Machine Learning to tackle some of our biggest technical challenges. We disambiguate complex supply chain problems and create ML and optimization solutions to solve those problems at scale. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, CA, Sunnyvale
Are you passionate about solving unique customer-facing problems in the Amazon scale? Are you excited about utilizing statistical analysis, machine learning, data mining and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The mission of Fit science team as part of Fashion Tech is to innovate and develop scalable ML solutions to provide personalized fit and size recommendation when Amazon Fashion customers evaluate apparels or shoes online. The team is hiring a Data Scientist who has a solid background in Statistical Analysis, Machine Learning and Data Mining and a proven record of effectively analyzing large complex heterogeneous datasets, and is motivated to grow professionally as a Data Scientist. Key job responsibilities - You will work on our Science team and partner closely with applied scientists, data engineers as well as product managers, UX designers, and business partners to answer complex problems via data analysis. Outputs from your analysis will directly help improve the performance of the ML based recommendation systems thereby enhancing the customer experience as well as inform the roadmap for science and the product. - You can effectively analyze complex and disparate datasets collected from diverse sources to derive key insights. - You have excellent communication skills to be able to work with cross-functional team members to understand key questions and earn the trust of senior leaders. - You are able to multi-task between different tasks such as gap analysis of algorithm results, integrating multiple disparate datasets, doing business intelligence, analyzing engagement metrics or presenting to stakeholders. - You thrive in an agile and fast-paced environment on highly visible projects and initiatives. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
US, WA, Seattle
Amazon is continuing to invest in its Advertising business to tap into the growing online advertising market. The Publisher Technologies team builds and operates extensible services that empower 1P Publishers to improve the monetization of their customer experiences, along with the experiences themselves. We bias toward standards-based and flexible designs that allow Publishers the ability to invent on top of our solutions and to interoperate well with other advertising technology providers; both internal and external. The Publisher Technology Data, Insights, and Analytics team enables faster data-driven decision making for Publishers and Monetization teams by providing them with near real time data, data management tools, actionable insights, and an easy-to-use reporting experience. Our data products provide Publishers and Monetization teams with the capabilities necessary to better understand the performance of their Advertising products along with supporting machine learning at scale. In this role, you will join a team whose data products and services empower hundreds of teams across Amazon with near real time data to support big data analytics, insights, and machine learning at scale. You will collaborate with cross-functional teams to design, develop, and implement advanced data tools, predictive models, and machine learning algorithms to support Advertising strategies and optimize revenue streams. You will analyze large-scale data to identify patterns and trends, and design and run A/B experiments to improve Publisher and advertiser experiences. Key job responsibilities - Design and lead large projects and experiments from beginning to end, and drive solutions to complex or ambiguous problems - Create tools and solve challenges using statistical modeling, machine learning, optimization, and/or other approaches for quantifiable impact on the business - Use broad expertise to recommend the right strategies, methodologies, and best practices, teaching and mentoring others - Key influencer of your team’s business strategy and of related teams’ strategies - Communication and documentation of methodologies, insights, and recommendations for senior leaders with various levels of technical knowledge We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA