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.

Related content
Matt Taddy, the chief economist for Amazon’s North America Consumer organization, talks about his recent book, and explains why economists should consider pursuing a career at the company.

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.

    Related publication
    We consider dynamic pricing with many products under an evolving but low-dimensional demand model. Assuming the temporal variation in cross-elasticities exhibits low-rank structure based on fixed (latent) features of the products, we show that the revenue maximization problem reduces to an online bandit convex optimization with side information given by the observed demands. We design dynamic pricing algorithms

    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.

    Related content
    Amazon's Daliana Liu helps others in the field chart their own paths.

    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.

    Access sample chapters

    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.

Research areas

Related content

NL, Amsterdam
Are you a MS or PhD student interested in a 2025 Internship in the field of machine learning, deep learning, speech, robotics, computer vision, optimization, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact, visionary person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Luxembourg, Netherlands, Poland, Romania, Spain, UAE, and UK). Please note these are not remote internships.
US, WA, Seattle
Come be a part of a rapidly expanding $35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. The AB Sales Analytics, Data, Product and Tech (ADAPTech) team uses CRM, data, product, and science to improve Sales productivity and performance. It has four pillars: 1) SalesTech maintains Salesforce to enable Sales workflows, and supports >2K users in nine countries; 2) Product and Science builds tools embedded with bespoke Machine Learning (ML) and GenAI large language models to enable sales reps to prioritize top accounts, position the right Amazon Business (AB) product features, and take actions based on critical customer events; 3) Sales Data Management (SDM) and Sales Account Management (SAM) enrich customer profiles and business hierarchies while improving productivity through automation and integration of internal/external tools; and 4) Business Intelligence (BI) enables self-service reporting simplifying access to key insights through WBRs and dashboards. Sales teams leverage these products to identify which customers to target, what features to target them with, and when to target them, in order to capture their share of wallet. A successful Applied Scientist at Amazon demonstrates bias for action and operates in a startup environment, with outstanding leadership skills, and proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. We need great leaders to think big and design new solutions to solve complex problems using machine learning (ML) and Generative AI techniques to improve our customers’ experience when using AB. You have hands-on experience making the right decisions about technology, models and methodology choices. Key job responsibilities As an Applied Scientist, you will primarily leverage machine learning techniques and generative AI to outreach customers based on their life cycle stage, behavioral patterns, and purchase history. You may also perform text mining and insight analysis of real-time customer conversations and make the model learn and recommend the solutions. Your work will directly impact the trust customers place in Amazon Business. You will partner with product management and technical leadership to identify opportunities to innovate customer journey 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 also play a crucial role in shaping strategies. Additional responsibilities include: -Design, implement, test, deploy and maintain innovative data and machine learning solutions to further the customer experience. -Create experiments and prototype implementations of new learning algorithms and prediction techniques -Develop algorithms for new capabilities and trace decisions in the data and assess how proposed changes could potentially impact business metrics to cater needs of Amazon Business Sales -Build models that measure incremental value, predict growth, define and conduct experiments to optimize engagement of AB customers, and communicate insights and recommendations to product, sales, and finance partners. A day in the life In this role, you will be a technical expert with significant scope and impact. You will work with Technical Product Managers, Data Engineers, other Scientists, and Salesforce developers, to build new and enhance existing ML models to optimize customer experience. You will prototype and test new ideas, iterate quickly, and deploy models to production. Also, you will conduct in-depth data analysis and feature engineering to build robust ML models.
US, WA, Bellevue
Amazon Web Services (AWS) offers a broad set of global compute, storage, database, analytics, application, and deployment services that help organizations move faster, lower IT costs, and scale applications. These services are trusted by the largest enterprises and the hottest start-ups to power a wide variety of workloads including web and mobile applications, data processing and warehousing, storage, archive, and many others. We are looking for an applied scientist to help us define and build a new enterprise application. AWS Applications is building services in Supply Chain Management and is looking for a scientist to tackle complex science problems in Supply Chain including demand planning, supply planning and sustainability which will be used by our customers across a wide range of industries. We operate a fast growing business and our journey has only started. Our mission is to build the most efficient and optimal supply chain software on the planet, using our science and technology as our biggest advantage. We aim to leverage cutting edge technologies in optimization, operations research, and machine learning to grow our businesses. As an Applied Scientist, you’ll design, model, develop and implement state-of-the-art models and solutions used by users worldwide. As part of your role you will regularly interact with software engineering teams and business leadership. The focus of this role is to research, develop, and deploy models to improve state-of-the-art for time series. You will have the opportunity to work on our assistant solution allowing our users to ask data questions in natural language and get intelligent insights and exceptions. Key job responsibilities Lead and partner with the engineering to drive modeling and technical design for complex business problems. Develop accurate and scalable machine learning models to solve our hardest supply chain problems. Lead complex modeling analyses to aid management in making key business decisions and set product direction. A day in the life Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
US, WA, Seattle
We are building GenAI based shopping assistant for Amazon. We reimage Amazon Search with an interactive conversational experience that helps you find answers to product questions, perform product comparisons, receive personalized product suggestions, and so much more, to easily find the perfect product for your needs. We’re looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away. This will be a once in a generation transformation for Search, just like the Mosaic browser made the Internet easier to engage with three decades ago. If you missed the 90s—WWW, Mosaic, and the founding of Amazon and Google—you don’t want to miss this opportunity.
US, WA, Seattle
Our team's mission is to improve Shopping experience for customers interacting with Amazon devices via voice. We work with Alexa and multiple other teams to research and develop advanced state-of-the-art speech technologies. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. Key job responsibilities We are looking for a passionate, talented, and inventive Research Scientist with a background in Machine Learning to help build industry-leading Speech and Language technology. As a Research Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech synthesis. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for Speech and Language applications. * Participate in research activities including the application and evaluation of Speech and Language techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business.
US, WA, Seattle
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve associate, employee and manager experiences at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science! The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. Key job responsibilities As an Applied Scientist for People Experience and Technology (PXT) Central Science, you will be working with our science and engineering teams, specifically on re-imagining Generative AI Applications and Generative AI Infrastructure for HR. Applying Generative AI to HR has unique challenges such as privacy, fairness, and seamlessly integrating Enterprise Knowledge and World Knowledge and knowing which to use when. In addition, the team works on some of Amazon’s most strategic technical investments in the people space and support Amazon’s efforts to be Earth’s Best Employer. In this role you will have a significant impact on 1.5 million Amazonians and the communities Amazon serves and ample scope to demonstrate scientific thought leadership and scientific impact in addition to business impact. You will also play a critical role in the organization's business planning, work closely with senior leaders to develop goals and resource requirements, influence our long-term technical and business strategy, and help hire and develop science and engineering talent. You will also provide support to business partners, helping them use the best scientific methods and science-driven tools to solve current and upcoming challenges and deliver efficiency gains in a changing marke About the team The AI/ML team in PXTCS is working on building Generative AI solutions to reimagine Corp employee and Ops associate experience. Examples of state-of-the-art solutions are Coaching for Amazon employees (available on AZA) and reinventing Employee Recruiting and Employee Listening.
CA, BC, Vancouver
Alexa Daily Essentials is hiring an Applied Scientist to research and implement large language model innovations to enhance Alexa's language understanding, knowledge representation, reasoning and generation capabilities. The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. We drive over 40 billion+ actions annually across 60 million+ monthly customers, who engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. Our upcoming launches are at the forefront of innovation, delivering step-function improvements in experiences that stretch across the customer journey, and new AI technologies that will enable customers to send Alexa information for future recall and conversation. We collaborate closely with partners such as Amazon.com, Whole Foods, Spotify, CNN, Fox, NPR, BBC, Discovery, and Food Network to deliver our vision. If you are passionate about redefining the personal assistant experience and leveraging innovative technology to improve daily life, we’d love to hear from you. This is an opportunity to make a tangible impact at the heart of the Alexa ecosystem. As an applied scientist, you will advance state of the art techniques in ML and LLM, and work closely with product and engineering teams to build the next generation of the Alexa smart assistant. Key job responsibilities - Rapidly prototype ML/LLM solutions, evaluate feasibility, and drive projects to production deployment - Continuously monitor and improve model performance through retraining, parameter tuning, and architecture refinements - Develop new training and inference techniques to improve model performance - Work cross-functionally across engineering, product, and business teams to understand customer needs, scope science work, and drive science solutions from conception to customer delivery - Research and develop LLM innovations, and lead paper publications. - Code proficiently in Python (required) and Java (preferred); optimize systems for high performance at scale; contribute code directly into production services - Innovate and develop science and engineering solutions that optimize team operations and increase team effectiveness. - Clearly communicate complex technical concepts to non-technical stakeholders and leadership
US, MA, North Reading
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers who work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling, and fun. Amazon Robotics empowers a smarter, faster, more consistent customer experience through automation. Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands. Amazon Robotics has a dedicated focus on research and development to continuously explore new opportunities to extend its product lines into new areas. Key job responsibilities We are seeking an enthusiastic Data Scientist to: - Design and implement state-of-the-art solutions for never-before-solved problems. - Collaborate closely with other research and robotics experts to design and run experiments, research new algorithms, and find new ways to improve Amazon Robotics analytics to optimize the Customer experience. - Partner with technology and product leaders to solve business problems using scientific approaches. - Build new tools and invent business insights that surprise and delight our customers. - Work to quantify system performance at scale, and to expand the breadth and depth of our analysis to increase the ability of software components and warehouse processes. - Work to evolve our library of key performance indicators and construct experiments that efficiently root cause emergent behaviors. - Engage with software development teams and warehouse design engineers to drive the evolution of the Amazon Robotics system, as well as the simulation engine that supports our work. A day in the life As a member of the Software Research and Data Science (SWRDS) team, there are multiple different paths you can take. For example, you may conduct deep dive analysis of both structured and unstructured robotics field analyses. Some of our team members focus on modeling, pipelines and tooling work, while others focus more on analytic work. We also have team members working on early-concept solution analysis (leveraging simulations, ML-based models, experimenting with new solutions). About the team The Amazon Robotics Software Research and Data Science (SWRDS) team builds and runs simulation experiments and delivers analyses that are central to understanding the performance of the entire Amazon Robotics system. This includes operational and software scaling characteristics, bottlenecks, and robustness to “chaos monkey” stresses -- we inform critical engineering and business decisions about Amazon’s approach to robotic fulfillment.
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities - Collaborate with AI/ML scientists and architects to Research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction About the team ABOUT AWS: Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
US, MA, North Reading
The Amazon Robotics (AR) Software Research and Science team builds and runs simulation experiments and delivers analyses that are central to understanding the performance of the entire AR system. This includes operational and software scaling characteristics, bottlenecks, and robustness to “chaos monkey” stresses -- we inform critical engineering and business decisions about Amazon’s approach to robotic fulfillment. We are seeking a Data Scientist to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. The Scientist will work closely with other research and robotics experts to design and run experiments, research new algorithms, and find new ways to improve Amazon Robotics analytics to optimize the Customer experience. They will partner with technology and product leaders to solve business problems using scientific approaches. They will build new tools and invent business insights that surprise and delight our customers. They will work to quantify system performance at scale, and to expand the breadth and depth of our analysis to increase the ability of software components and warehouse processes. They will work to evolve our library of key performance indicators and construct experiments that efficiently root cause emergent behaviors. They will engage with software development teams and warehouse design engineers to drive the evolution of the AR system, as well as the simulation engine that supports our work. Key job responsibilities We are seeking an enthusiastic Data Scientist to design and implement state-of-the-art solutions for never-before-solved problems. The DS will collaborate closely with other research and robotics experts to design and run experiments, research new algorithms, and find new ways to improve Amazon Robotics analytics to optimize the Customer experience. They will collaborate with technology and product leaders to solve business problems using scientific approaches. They will build new tools and invent business insights that surprise and delight our customers. They will work to quantify system performance at scale, and to expand the breadth and depth of our analysis to increase the ability of software components and warehouse processes. They will work to evolve our library of key performance indicators and construct experiments that efficiently root cause emergent behaviors. They will collaborate with software development teams and warehouse design engineers to drive the evolution of the AR system, as well as the simulation engine that supports our work. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!