The book cover of the recently released Modern Business Analytics textbook,  and photos of its coauthors, Matt Taddy, Leslie Hendrix, and Matthew C. Harding.
Matt Taddy (top right), vice president of Amazon's Private Brands business, is the coauthor of a recently released data-science textbook, Modern Business Analytics, along with Leslie Hendrix (middle right), and Matthew C. Harding.

New data-science textbook explains the ‘why’, rather than the ‘what’ of decision-making

Matt Taddy, vice president of Amazon’s Private Brands business, is the coauthor of Modern Business Analytics: Practical Data Science for Decision Making, a primer for those who want to gain the skills to use data science to help make decisions in business and beyond.

When Matt Taddy earned his PhD in applied mathematics and statistics from the University of California, Santa Cruz, in 2008, the notion of a data-science specialization was still in its infancy.

Today, the business-analytics profession, or the discipline of using data to make business, public policy, public health, and other decisions, is blossoming, and Taddy is excited about how the field is becoming more multi-disciplinary, incorporating statistics, machine learning, economics, and even the social sciences.

“I benefitted from getting involved in the early stages before it became more specialized,” says Taddy.

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Since earning his PhD, Taddy has been a research assistant at NASA Ames Research Center and Sandia National Laboratories, a research fellow at eBay, the head of economics and data science for Business AI at Microsoft, a professor of econometrics and statistics at the University of Chicago Booth School of Business, the chief economist for Amazon’s North America Consumer organization, and now vice president of Amazon’s Private Brands business. His first textbook, Business Data Science, was published by McGraw Hill in 2019. At the time, he told Amazon Science that he began work on the book ten years prior when teaching a class of MBA students at the University of Chicago.

“I realized that there was an appetite for the material covered in the book from people who weren’t specialists in statistics or machine learning,” he said. “This idea that we could teach this material to non-specialists really motivated me not to just write this book, but also to push for changing the curriculum at the University of Chicago.”

Since publishing that textbook in 2019, his role at Amazon has evolved as has his interest in making great decisions from data. The result is a new textbook, Modern Business Analytics: Practical Data Science for Decision Making, which Taddy co-authored with Leslie Hendrix, PhD, associate professor at the Darla Moore School of Business at the University of South Carolina, and Matthew C. Harding, PhD, professor of economics and statistics at the University of California, Irvine.

According to the authors, “This book is a primer for those who want to gain the skills to use data science to help make decisions in business and beyond. The modern business analyst uses tools from machine learning, economics, and statistics to not only track what has happened but predict the future for their businesses.”

McGraw Hill, the book’s publisher, says: “This new higher-ed text takes a practical, modern approach to data science and business analytics for the analytics student and professional. It gives students the opportunity to learn by doing, with real data analysis examples that explain the ‘why’, rather than the ‘what’ in decision-making discussions. It uses R as the primary technology through the text and includes an end-of-chapter reference to the basic R recipes in each chapter. Modern Business Analytics: Practical Data Science for Decision Making has crossed the boundaries and created something truly interdisciplinary.”

Amazon Science connected with Taddy to discuss how his thinking about the topic has evolved in the past three years, his belief that deeper business decisions require focusing on why things happen versus what has happened, and how he’s applying modern business analytics techniques in running Amazon’s Private Brands business.

  1. Q. 

    In 2019 you authored Business Data Science that brought together concepts from statistics, machine learning, and the social sciences to help businesses use data more effectively. How has your thinking evolved in the past three years? And how does Modern Business Analytics address that?

    A. 

    Modern Business Analytics is a direct follow to Business Data Science. From Business Data Science we learned there is an audience, but I received feedback from a number of professors who, for example, were teaching from Business Data Science for MBA classes, or advanced undergraduate data-science classes, or master’s in public policy programs, that we didn’t really deliver the content in a format that was accessible to a broader audience.

    McGraw Hill approached me again and said there was an opportunity to do a better job serving a wider audience and asked if I would be interested. My response was ‘Of course. One reason I did the book initially was to try and hit the widest audience possible.’ Recognizing that I’m busy with my day job here at Amazon, McGraw Hill suggested I approach co-authors to help with content development. Fortunately, both Leslie and Matthew agreed to contribute. Matthew teaches from this book in an MBA program at the University of California, Irvine, and Leslie teaches from a version of this book for a business-analytics program at the University of South Carolina.

    They've both experienced the challenges of onboarding students who have no exposure to programming languages, or students who are less proficient in math than the students I was originally exposed to when I wrote the material for Business Data Science. Leslie and Matthew brought a great new perspective to the project. Generally, you're never happy with the first version of anything. Leslie and Matthew helped simplify some of the explanations provided in the previous book and contributed more examples. From my experience this is what students benefit from the most. The result: we were able to include many more real-world examples into Modern Business Analytics and make the new book far more accessible to a broader audience.

    In education it often takes a while for someone to develop an introductory-level book that pulls material from multiple disciplines and brings readers to the current state of the art. That’s what we challenged ourselves to deliver here. Our audience is anyone who wants to get the skills to use modern large-scale data to make decisions, whether they are in business, government, science, or anywhere else.

  2. Q. 

    It would seem that today's modern business analyst must be multidisciplinary, with machine learning, economics, statistics, and other skills. What’s the skill set you look for?

    A. 

    I haven’t found an individual with all of those ingredients in equal measure. It is more about how you build a team with a diversity of skills and backgrounds. Data scientists, research scientists, applied scientists, and economists all use the tools that we discuss in the book. When you’re building a team focused on making decisions from data, you don’t want individuals with the same skills. You want individuals with different levels of emphasis. Some are going to have a much stronger background in computer science. They're going to understand the algorithms component better. Others are going to have a stronger background in uncertainty quantification and the mathematics of what I refer to as modern statistics. Some will have an economics background element. Others will be comfortable addressing causal inference and structural analysis.

    What’s been really exciting about data science in the past 10 years is that we've created a common vocabulary so individuals from many disciplines can talk to each other. Today, you can build a team that has economists, applied scientists, research scientists, machine learning engineers, and data scientists working together to address a common challenge. When I first got into data science more than a decade ago this common vocabulary didn’t exist. There was a real boundary to working on data across disciplines. Fortunately, much of this has gone away. Now the economists and the machine-learning practitioners speak the same lingo making it much easier to build the diverse teams required to make decisions from data.

    I mentioned causal inference and structural analysis previously and want to point out another aspect of the book that is unique. A lot of work inside a tech company can focus on pure prediction, what I would consider standard machine learning problems where you want to discover patterns in correlation. For a broader audience beyond machine learners, we need to understand how to make policy decisions – how to use data to decide between option A or option B. For that type of decision-making you really have to get into the structure of why things are happening.

    I took that seriously in the first book and doubled down on it with this book. For example, we have a chapter that’s dedicated to either fully randomized experiments or quasi experimental settings. These are A/B experiments, or what we refer to at Amazon as Weblabs. If you’re familiar with these experiments you know they aren’t nearly as simple as the term A/B implies. There's a lot of complexity to these experiments — how you run them, how you analyze them. As a result, we focus a lot of attention on how to structure these A/B style trials and how you analyze data that has some experimental randomization as part of it.

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    Another chapter is devoted to understanding why things are happening when you’re working from purely observational data. Here we go deep into some of the methods we use heavily in industry — orthogonal or double machine learning using high dimensional control sets and other things such as synthetic controls. This chapter codifies the methods for utilizing causal analysis and structural analysis in observational settings. As mentioned previously, I think this differentiates the book from others for this audience. Causal inference can be intimidating and you don’t often see it addressed at this level. It will be a high bar for some students, but the feedback we’ve received from professors who are teaching from early versions of our work is that students, especially those with some industry experience, are really attracted to the material. The students are attracted to it because they have worked in industry and know how important it is to be able to properly conduct experiments and perform causal analyses.

  3. Q. 

    Can you provide some context about Amazon’s Private Brands business, and how you’re applying modern business analytics to make better decisions for your business.

    A. 

    I took over the role leading our worldwide Private Brands organization within the last year and much of what we do is seemingly simple, straightforward customer-centric product development. When I think about our business, I think about what product assortment we need in the long term. To determine this, I have to understand what products our customers expect from Amazon private brands and what they are able to get in private-label format from our competitors. Those are fairly straightforward business questions to address. Our customers expect us to have really attractive prices, so we need to ensure that our customers find sharp every-day low prices for the products that we're providing. And our customers expect quality. We need to make sure that they are getting the quality they expect and that our manufacturers are getting feedback from customers that allows them to produce better products.

    That all sounds like pretty vanilla stuff. I could be talking about any number of MBA case studies and all of our competitor retailers are asking similar questions of their private label businesses.

    I want us to build the ML services that allow us to quickly determine from customer feedback where there might be issues or opportunities anywhere in our production.
    Matt Taddy

    But then I think about how I can use data and science to help me make the right decisions. Go back to my first question. How do I understand what customers expect to find? It’s not straightforward. Can the data tell us that our customers perceive our product as competitively priced even though it comes in a different bottle, it's got a different formulation, and there might be quality differences? It turns out that, yes, we can use data and ML to understand how customers evaluate the value proposition of our products. This information is useful both in how we build products and how we price them.

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    Another idea that’s super exciting to me, and which seems obvious, is you need customer feedback to improve your products. At Amazon we get feedback on a very large scale. We get it through customer reviews. We can use ML and statistics to dive deep into that information and use it to produce anecdotes and feedback signals that we use to improve the quality, pricing, and overall customer experience for our products. All of our competitor retailers building private label products are asking the same questions about how to improve products for customers. But at Amazon Private Brands we’re asking how we can do this analysis faster and in a more automated fashion to quickly get the insights back to our manufacturers.

    Today, we implement traditional quality processes that you would expect from any large manufacturer. We are with the best in class there. That said, we can go much further filtering all of the customer information we're getting through reviews and use it to inform our manufacturing partners to start this process of continual improvement and close the gap between customers and manufacturers. I want us to build the ML services that allow us to quickly determine from customer feedback where there might be issues or opportunities anywhere in our production. We make shampoo. We make toilet paper. We make batteries. We make T-shirts. We make a large variety of products, and we come at it from a very Amazonian point of view which is to apply a data-centric mindset. And that, in turn, leads us to concepts from the book.

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    Want to explore learn more about the recently released textbook Modern Business Analytics ? Click here to learn more about each chapter, and to access sample chapters.

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Interested in building something new? Join the Amazon Autos team on an exhilarating journey to redefine the vehicle shopping experience. This is an opportunity to be part of the ground floor team for one of Amazon's new business ventures. As a key member, you'll lead the science strategy and play a pivotal role in helping us achieve our mission. Our goal is to create innovative automotive discovery and shopping experiences on Amazon, providing customers with greater convenience and a wider selection. If you're enthusiastic about innovating and delivering exceptional shopping experiences to customers, thrive on new challenges, and excel at solving complex problems using top-notch ML models, LLM and GenAI techniques, then you're the perfect candidate for this role. Strong business acumen and interpersonal skills are a must, as you'll work closely with business owners to understand customer needs and design scalable solutions. Join us on this exhilarating journey and be part of redefining the vehicle shopping experience. Key job responsibilities As Senior Applied Scientist in Amazon Autos, you will: - Lead the roadmap and strategy for applying science to solve customer problems in the Amazon AutoStore domain. - Drive big picture innovations with clear roadmaps for intermediate delivery. - Determine which areas of research to invest in. - Effectively communicate complicated machine learnings concepts to multiple partners. - Identify when to leverage existing technology versus innovate a new technology. - Work closely with partners to identify problems from the customer's perspective. - Interface with business customers, gathering requirements and delivering science solutions. - Apply your skills in areas such as deep learning and reinforcement learning while building scalable solutions for business problems. - Produce and deliver models that help build best-in-class customer experiences and build systems that allow us to deploy these models to production with low latency and high throughput. - Utilize your Generative AI, time series and predictive modeling skills, and creative problem-solving skills to drive new projects from ideation to implementation. - Establish scalable, efficient, automated processes for large scale data analyses, model development, validation and implementation. We are looking for a Senior Applied Scientist who loves working with big data and is passionate about improving the customer shopping experience. A day in the life In this role, you will be part of a multidisciplinary team working on one of Amazon's newest business ventures. As a key member, you will collaborate closely with engineering, product, design, operations, and business development to bring innovative solutions to our customers. Your science expertise will be leveraged to research and deliver novel solutions to existing problems, explore emerging problem spaces, and create new knowledge. You will invent and apply state-of-the-art technologies, such as large language models, machine learning, natural language processing, and computer vision, to build next-generation solutions for Amazon. You'll publish papers, file patents, and work closely with engineers to bring your ideas to production. Additionally, you will mentor Applied Scientists and Software Development Engineers with an interest in machine learning. This is an opportunity to make a significant impact, working in partnership with teams across Amazon to create enormous benefits for our customers through cutting-edge products. About the team This is a critical role for a newly formed team with a vision to create innovative automotive discovery and shopping experiences on Amazon, providing customers better convenience and more selection. We’re collaborating with other experienced teams at Amazon to define the future of how customers research and shop for cars online.
US, WA, Seattle
Enterprise Engineering is seeking an exceptional Senior Applied Scientist to join our AppSense team, which is revolutionizing Software Asset Management at Amazon and beyond. As a key member of our applied science team, you will leverage cutting-edge machine learning, natural language processing, and data analytics techniques to solve complex challenges in software discovery, cost optimization, and intelligent decision-making. Your work will directly impact Amazon's ability to manage its vast software portfolio efficiently, driving significant cost savings and operational improvements. In this role, you will have the opportunity to invent and implement novel scientific approaches that address critical business problems at the product level. You will collaborate closely with product managers, engineers, and business stakeholders to translate scientific innovations into practical, scalable solutions that enhance AppSense's capabilities and deliver value to our customers. Key job responsibilities * Lead the design, implementation, and delivery of scientifically complex solutions for AppSense, focusing on areas such as automated software discovery, intelligent cost optimization, and predictive analytics * Develop and apply state-of-the-art machine learning models to improve software categorization, usage prediction, and anomaly detection * Create innovative natural language processing solutions for contract analysis, optimization, and automated report generation * Design and implement advanced recommendation systems for software stack optimization based on job roles and team compositions * Develop reinforcement learning algorithms for automated license management, including predictive maintenance to prevent unexpected expirations or overage charges * Develop AI-driven negotiation assistants and collaborative budgeting tools with ML-powered spend forecasting * Create sentiment analysis models to gauge software satisfaction from user feedback and support tickets About the team The AppSense team is at the forefront of transforming software asset management at Amazon. We're building a comprehensive platform that provides visibility, control, and optimization for Amazon's vast software portfolio. Our mission is to leverage cutting-edge technology to help businesses discover, manage, and optimize their software assets, driving significant cost savings and operational efficiencies. As part of the applied science team within AppSense, you'll work alongside talented scientists, engineers, and product managers who are passionate about solving complex problems at scale. We foster a culture of innovation, encouraging team members to push the boundaries of what's possible in software asset management. Your contributions will directly impact Amazon's bottom line and have the potential to shape the future of how organizations manage their software ecosystems.
US, WA, Seattle
** This position is open to all candidates in Palo Alto, CA, Seattle, WA, NYC and Arlington, VA ** Amazon Ads Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Machine Learning Applied Scientist who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems. Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. We are looking for a talented Machine Learning Applied Scientist for our Amazon Ads Response Prediction team to grow the business. We are providing advanced real-time machine learning services to connect shoppers with right ads on all platforms and surfaces worldwide. Through the deep understanding of both shoppers and products, we help shoppers discover new products they love, be the most efficient way for advertisers to meet their customers, and helps Amazon continuously innovate on behalf of all customers. Key job responsibilities As a Machine Learning Applied Scientist, you will: * Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities * Develop scalable and effective machine-learning models and optimization strategies to solve business problems * Run regular A/B experiments, gather data, and perform statistical analysis * Work closely with software engineers to deliver end-to-end solutions into production * Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving * Conduct research on new machine-learning modeling to optimize all aspects of Sponsored Products business About the team We are pioneers in applying advanced machine learning and generative AI algorithms in Sponsored Products business. We empower every customer with a customized discovery experiences from back-end optimization (such as customized response prediction models) to front-end CX innovation (such as widgets), to help shoppers feel understood and shop efficiently on and off Amazon.
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.