Andrew Borthwick
Andrew Borthwick, an Amazon principal scientist, shares his insights related to helping organize a company-wide challenge for one of the company's internal science events, and on how, despite the company's decentralized approach to science and engineering, the company still fosters collaboration and a sense of community among scientists.
Credit: Andrew Borthwick

Fostering a culture of innovation

An Amazon principal scientist describes how an internal challenge has fostered greater collaboration and a sense of community among the company’s scientists.

Editor’s Note: Andrew Borthwick is a principal scientist at Amazon; he leads a team focusing on challenges of automatic machine learning over Amazon’s expansive product catalog. In this article, he describes his experience in helping organize a Challenge within the company’s annual, internal machine-learning conference, which brings together thousands of scientists and engineers from across the company to showcase their work, network with peers, and raise the quality of science at the company.

More than 4,000 scientists and engineers attended last fall’s virtual, online event, with the opportunity to view keynote, oral paper, and poster presentations, along with workshops, training sessions, and other activities.

In this article, Borthwick shares his experience in helping organize one of the conference’s Challenge events, and provides insight into how, despite the company’s highly decentralized approach to science and engineering, the company fosters collaboration and a sense of community among scientists.

There is a huge amount of innovation in machine learning at Amazon. So much, in fact, that it can be difficult to keep track of all of the cool ideas percolating among teams. To help Amazonians push the state of the art forward, we have an annual internal Amazon Machine Learning Conference (AMLC). This conference is structured similarly to well-known academic conferences, with a process of papers being peer reviewed, and a high bar for acceptance.

I’ve been working in machine learning at Amazon for six years now and have served as a reviewer and meta-reviewer of papers for AMLC many times. Although reviewing papers has been a stimulating opportunity in that it has allowed me to see the great diversity of machine learning research here at Amazon, I sometimes found myself stymied when deciding on the merits of an idea.

There is a huge amount of innovation in machine learning at Amazon. So much, in fact, that it can be difficult to keep track of all of the cool ideas percolating among teams.
Andrew Borthwick

Amazon is well known for a culture of “two pizza teams”. We try to reduce Amazon’s very large scale into chunks of work that can be attacked by a team of people small enough that they can be fed with two pizzas (in practice these teams are typically five to eight in size, so the pizzas should definitely be large). Each team can then be customer obsessed in focusing on the opportunity they are targeting. In machine learning, this has a major advantage in allowing us to be agile — we don’t spend too much time coordinating with other teams — so teams are free to experiment with approaches. The downside to this approach is that it can lead to a duplication of effort, and an inability to identify the best scientific approach.

I have frequently reviewed papers that presented data where some team had greatly increased the accuracy of their machine learning algorithm relative to their previous approach, and had  delivered significant customer value.  This sounds good, but one of the Amazon Leadership Principles is that we should “Insist on the Highest Standards”. I would ask myself, “Yes, what this paper is describing is great, but is this the best that could be done here?”

The problem was most acute when you had separate two-pizza teams working on very similar challenges. One of my areas of expertise is in linking records in databases, which led to my work on AWS Lake Formation FindMatches. We’re doing some really interesting science in this area:  one team is working on finding duplicate items in Amazon’s product catalog while another is working on identifying sets of products that are variants of one another (when buying Amazon Essentials Crewneck t-shirts, for instance, you will see all the different colors and sizes on the same page). These problems are similar in that a customer might want to see if two products “match”, but in one case they are looking for an “exact match”, while in the other they want to find “products that match if you ignore color and size differences”.

We had a similar issue with machine learning classification problems.

One two-pizza team was working on the problem of classifying Amazon products as to which customer-facing product type they belong to (such as “women’s sneakers”). Meanwhile another team was classifying items into categories that sometimes have a special treatment for sales tax purposes (for instance “alcoholic beverage” or “children’s clothing” or “food” or “medicine”). Amazon Music has a similar problem with classifying music tracks as to genre (is it “holiday music” or “instrumental jazz” or “string quartet”?).

Each of these teams was working on classifying items into a fairly large, but fixed number of classes, a problem known in machine learning as “k-way classification”. The items being classified (either products or music tracks) had many different attributes which were of different data types such as text (product_description, music_track_title), numeric (shipping_weight), categorical (color, size), and image (the picture of the product or the album cover), so we said that this was “k-way classification of multimodal tabular data”. Finally, each of these teams had a substantial number of labeled records where an Amazon employee had determined the correct category. We dubbed this challenge as “supervised k-way classification of multimodal tabular data” —  a very important but understudied problem in ML.

The problem came when each of these teams submitted a paper describing their results to the Amazon Machine Learning Conference.  The questions I had to resolve as a reviewer were: “Who has the better algorithm”? and “This other two-pizza team is working on a very similar problem. What would happen if they used the other team’s algorithm on their data”?

AMLC Panel Discussion
The MultiModal Tabular Data Challenge Workshop included a question-and-answer session with competition finalists and scientists from the competition's organizing committee.

These kinds of questions led some of my machine learning colleagues and me to organize an internal “Grand Challenge in MultiModal Tabular Data”. Organizing a competition like this is a big task, but there are similar examples in the global ML community. Our first project was to gather and organize k-way classification and matching datasets from two-pizza teams across Amazon.

Next we had a kick-off meeting where we announced the competition and the prizes ($1000 in Amazon gift cards for the best average performance on the matching tasks and the best average performance on the classification tasks).

The contest itself lasted for four months, with more than 50 teams submitting results, and culminated with a workshop at AMLC last October. There the top three teams in the Matching and K-Way Classification challenges described their systems.

In reflecting on the Challenge, we found a number of positive effects:

  • The competition was a fun activity, with more than 50 teams and over 100 participants. Many participants enthusiastically made dozens of attempts at the different competitions.
  • Because a reverence for rank and titles is not one of Amazon’s Leadership Principles, the Challenge placed participants of all levels, locations, and job titles on equal footing.
  • One of the key challenges for the organizing committee was the need to standardize all of the data for the different tasks according to the same conventions (for instance, we made all of the data available with similar schemas in two popular formats —.csv and .parquet). This data is now available for future Amazon research projects, and thus future papers submitted to the conference.  
  • Two of the top six solutions made heavy use of AWS’ new open source Automated Machine Learning toolkit, AutoGluon, including one of the Grand Prize winners. Ideas from these Challenge entrants also made their way back into the AutoGluon toolkit, particularly around improving AutoGluon’s ability to handle textual columns in a tabular dataset.
  • Researchers benefited because these datasets are more complex and representative of real-world problems than most datasets in the public domain. In particular, it is difficult for researchers to get their hands on datasets where the correct decision hinges on signals derived from a combination of complex text, image, numeric, and categorical attributes.
  • More generally, the Challenge has helped to encourage closer teamwork among  different two-pizza teams working on similar problems. I’ve been in a number of meetings with teams working on a task that was in the Challenge or on problems that were similar to one of those tasks, where we have discussed ideas for leveraging the learnings from the winning teams.
  • Finally, for me, the Challenge led me to join the Amazon Selection and Catalog Systems team, which was one of the main contributors of data to the project. One of the great things about working here is the opportunity to switch to a team that you are passionate about.
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Take the earth's most customer-centric company. Mix in millions ofshoppers spending billions of dollars annually and an opportunity to useyour skills in machine learning and data mining to improve productrecommendations and search. What do you get? The best job in theinternet today - PERIOD.The charter of Amazon’s Personalization team is to recommend the “right" product to the “right" customer at the “right" time. We generatepersonalized product recommendations for millions of customers each day, in a blink of an eye, thousands of times a second.If you are a strong software engineer with a background in MachineLearning, who is passionate about turning massive amounts of data intoactionable insights, then this is the right opportunity for you.You will work with a team of highly skilled and motivated scientists andengineers, who are building the next generation of personalizationproducts at Amazon. Our team advances the state-of-the-art inpersonalization by using machine learning, and leveraging Amazon’s vast computing resources (AWS) and data. As part of your job, you will deal with large amounts of training data, rapidly prototype new models that meet stringent performance requirements, and perform offline and online testing.Major responsibilities:- Research and use of statistical techniques to create scalablesolutions for business problems- Analyze and extract relevant information from large amounts ofAmazon's historical business data to help automate and optimize keyfeatures and processes- Work closely with scientists and engineering teams to create anddeploy new features- Work closely with stakeholders to optimize various business operations- Establish scalable, efficient, automated processes for large scaledata analyses, model development, validation and implementation.- Track general business activity and provide clear, compellingmanagement reporting on a regular basisBy submitting your application here, you can apply once to be considered for multiple Software Engineering openings across various Amazon teams. If you are successful in passing through the initial application review and assessment, you will be asked to submit your career and personal preferences so that our dedicated recruiters can match you to the right role based on these preferences.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/usAmazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual OrientationBy submitting your application here, you can apply once to be considered for multiple Software Engineering openings across various Amazon teams. If you are successful in passing through the initial application review and assessment, you will be asked to submit your career and personal preferences so that our dedicated recruiters can match you to the right role based on these preferences.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/usAmazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
US, WA, Seattle
Amazon Business (www.amazon.com/business) is an online store that combines the selection, convenience and value customers have come to know and love from Amazon, with new features and unique benefits tailored to the needs of businesses. Amazon Business provides easy access to hundreds of millions of products – everything from IT and lab equipment to education and food-service supplies. Amazon Business customers also enjoy a variety of benefits, including business-only pricing and selection, a multi-seller marketplace, single- or multi-user business accounts, approval workflow, purchasing system integrations, payment solutions, tax exemptions, dedicated customer support, Business Prime and more.Business Prime is a paid subscription service for verified business, charity or government organizations that provides the familiar Prime shopping experience end users love at home, with pricing plans and benefits that are suited for work. Business Prime now serves over a million employees in organizations throughout the world. Our customers range from government entities with tens of thousands of users to sole proprietors.The Business Prime team is looking for an experienced and motivated Data Scientist to generate data-driven insights influencing the Business Prime direction, build the necessary predictive models, optimization algorithms and customer behavioral segments allowing us to discover and expand the value proposition of Business Prime to the customers. Specific responsibilities include:- Customer lifecycle analysis improving targeting, customer identification, and spend behavior.- Data driven insights to accelerate acquisition of new members.- Grow benefits adoption based on customer segment, vertical, and drive customers to their "aha moment".- Predict customers at risk of churn and declining engagement.- Identify ineligible accounts including fraud, abuse, and other undesired behaviors.- Experiments to enhance membership value for B2B customers.In this role, you will be a technical expert with significant scope and impact. You will work with Business Intelligence Engineers, Financial Analysts, Product Managers, Software Engineers, Data Engineers, and other Data Scientists, to build new and enhance existing ML models to optimize customer experience. The successful Data Scientist will have extreme bias for action needed in a startup environment, with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career.
US, WA, Seattle
Amazon strives to be Earth's most customer-centric company. To achieve this, we hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.Economists for Amazon will be expected to work directly with senior management on key business problems faced in retail and marketing. The role allows you to influence company-wide strategy by applying the frontier of economic thinking to forecasting, program evaluation, customer behavior and other areas. Economists at Amazon will be expected to develop new techniques to process large data sets, use econometric models and methods to address complex problems, design and test hypothesis on customer pain points and key growth ideas and contribute to design of automated systems around the company. Economists in our team will work closely with other research scientists, machine learning experts, and economists globally to design new frameworks for understanding business outcome and what impacts customer experiences. Our economists and scientists work closely with software engineers to put algorithms into practice.We are an interdisciplinary team on the cutting edge of economics, statistical analysis, and machine learning whose mission is to solve AI and ML problems that have high risk with high returns Our team seeks an outstanding econometrician with demonstrated experience tackling large-scale time-series forecasting challenges. We prize creative problem solvers with the ability to adapt and extend cutting-edge forecasting models to the unique and interesting problems we have in measuring customer experiences and Amazon business outcome. Our problems include forecasting key customer experiences metrics at different levels of granularities and explaining the drivers of the changes in these metrics. The ideal candidate combines acumen in statistics and applied time-series modeling to grapple with these and other challenges and guide decision-making at the highest levels of accuracy and model explain-ability.
US, CA, Cupertino
Amazon's Simple Storage Service (S3) offers industry-leading scalability, data availability, security, and performance. S3’s Automated Reasoning Group (S3-ARG) develops and applies automated reasoning techniques to deliver correct, secure, durable, and available distributed systems and storage services. S3 is complex: it consists of over 300 microservices each of which is a distributed system. With a very large number of servers and 10s of millions of requests per second, it is also highly concurrent. S3 has a sizable codebase, developed and maintained by a large team of engineers. Getting such a complex and fast-evolving system right requires developing cutting edge automated reasoning methods that are continuously integrated into the software development process. This is what our team does.We work on techniques ranging from deductive proofs to model checking, from static analysis to runtime verification of protocols. We work both at the design and the code level, and connecting the two is essential for us. We partner with development teams to make sure that our methods are deployed across S3 and that correctness is maintained as the software evolves. We have had significant success with adoption and we are key contributors to recent S3 launches such as strong consistency. We are developing innovative methods all the time. We publish our results at conferences and journals.We are a diverse team and are looking for teammates who are enthusiastic to work on these problems and further the state of the art with us. We are seeking candidates who are deep in one area of expertise but also broad enough to take on the most complex cloud computing challenges. If you are interested in exploring, please e-mail s3-arg-jobs@amazon.com.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 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
At Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their Smart Devices such as Amazon Show, Spot, Echo, Dot or Tap. Our Services allow you to shop, no matter where you are or what you are doing, you can go from 'I want that' to 'that's on the way' in a matter of seconds. We are seeking the industry's best to help us create new ways to interact, search and shop. Join us, and you'll be taking part in changing the future of everyday lifeWe are seeking a Data Scientist to be part of the NLU science team for Alexa Shopping. This is a strategic role to shape and deliver our technical strategy in developing and deploying NLU, Machine Learning solutions to our hardest customer facing problems. Our goal is to delight customers by providing a conversational interaction. These initiatives are at the heart of the organization and recognized as the innovations that will allow us to build a differentiated product that exceeds customer expectations. We're a high energy, fast growth business excited to have the opportunity to shape Alexa Shopping NLU is defined for years to come. If this role seems like a good fit, please reach out, we'd love to talk to you.This role requires working closely with business, engineering and other scientists within Alexa Shopping and across Amazon to deliver ground breaking features. You will lead high visibility and high impact programs collaborating with various teams across Amazon. You will work with a team of Language Engineers and Scientists to launch new customer facing features and improve the current features.
US, NY, New York
MULTIPLE POSITIONS AVAILABLEEntity: Amazon Web Services, Inc.Title: Data Scientist ILocation: New York, NYPosition Responsibilities:Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLEEntity: Amazon.Com Services LLCTitle: Applied ScientistWorksite: Seattle, WAPosition Responsibilities:Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, CO, Denver
Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help public sector, medical center and non-profit agencies derive business value through the adoption of Artificial Intelligence (AI)? Eager to learn from many different enterprise’s use cases of AWS ML and DL? Thrilled to be key part of Amazon, who has been investing in Machine Learning for decades, pioneering and shaping the world’s AI technology?At Amazon Web Services (AWS), we are helping large enterprises build ML and DL models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. Our Professional Services organization works together with our AWS customers to address their business needs using AI.AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML or DL models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.If you do not live in a market where we have an open Data Scientist position, please feel free to apply. Our Data Scientists can live in any location where we have a Professional Service office.A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions. It will be a person who likes to have fun, loves to learn, and wants to innovate in the world of AI. Major responsibilities include:· Understand the customer’s business need and guide them to a solution using our AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances .· Assist customers by being able to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.· Use Deep Learning frameworks like PyTorch, Tensorflow, and MxNet to help our customers build DL models.· Use SparkML and Amazon Machine Learning (AML) to help our customers build ML models.· Work with our Professional Services Big Data consultants to analyze, extract, normalize, and label relevant data.· Work with our Professional Services DevOps consultants to help our customers operationalize models after they are built.· Assist customers with identifying model drift and retraining models.· Research and implement novel ML and DL approaches, including using FPGA.· This position can have periods of up to 10% travel.
US, WA, Seattle
At Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their Smart Devices such as Amazon Show, Spot, Echo, Dot or Tap. Our Services allow you to shop, no matter where you are or what you are doing, you can go from 'I want that' to 'that's on the way' in a matter of seconds. We are seeking the industry's best to help us create new ways to interact, search and shop. Join us, and you'll be taking part in changing the future of everyday lifeWe are seeking a Data Scientist to be part of the Correction and Automated Recovery (CARe) team for Alexa Shopping. Our team focuses on solving the hard problem of recovering from shopping errors in the Alexa pipeline and improve customer CX. The solutions will help address unique shopping challenges and maximize the self-learning benefits for shopping. This is a strategic role to shape and deliver our science strategy in developing and deploying Machine Learning solutions to our hardest customer facing problems. The initiatives are at the heart of the organization and recognized as the innovations that are ground breaking and will allow us to exceed customer expectations. If this role seems like a good fit, please reach out, we'd love to talk to you.This role requires working closely with business, engineering and other scientists within Alexa, Shopping and across Amazon to deliver ground breaking features. You will lead high visibility and high impact programs collaborating with various teams across Amazon. You will work with a team of SDEs and Scientists to launch new customer facing features and improve the current features.
US, MA, Cambridge
The 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.About the hiring groupThe Alexa AI team has a mission 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.Job responsibilitiesAs an Applied Scientist with the Alexa AI team, 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.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.