A head and shoulders profile photo of Haluk Demirkan, senior manager of Devices Demand Science
Haluk Demirkan, senior manager of Devices Demand Science, says his goal at Amazon "is to build an ecosystem in which technology is doing the labor-intensive tasks, freeing my team to do more smart work and value-added tasks."

How Haluk Demirkan is using ML-powered forecasts to get the right devices to the right place at the right time

Part-time sabbatical plan turns into full-time role for author of five books and more than 170 research articles.

For years, Amazon has been at the forefront of machine learning and data science. At the same time, the company has pioneered the large-scale automation of processes at all levels of its supply chains. But in its fast-moving commercial world, the constant challenge is to integrate these complementary fields to create processes that optimize the delivery of customer value.

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In 2021, Haluk Demirkan joined Amazon to boost the company’s efforts for that kind of integration. As the senior manager of Devices Demand Science, Demirkan is building and leading a team dedicated to using cutting-edge data analytics, machine learning, and process optimization — among other techniques — to transform sales predictions for Amazon devices, from the Kindle, the Echo family of devices, and Fire Tablets, to Fire TVs, and Ring Video Doorbell — an enormously important task.

To understand why the company’s Devices organization is excited about Demirkan’s arrival, a little background is required. Demirkan first came to the US from Turkey in 1991 on a three-month language course to improve his English. Three decades later, he hasn’t left. Instead, he has built a pioneering transdisciplinary career at the intersection of data science, service science, smart machines, and industry.

Bridging industry and academia

He earned a master’s in industrial and systems engineering and in 2002 completed a dual-degree PhD in information systems and operations management at the University of Florida. He gained these qualifications while simultaneously working full-time for AT&T Bell Labs (as it was then known) and Citibank in data analytics, process engineering, and price and supply chain optimization.

By the time he earned his PhD, Demirkan had already spent 11 years in industry, so in 2002 he decided to give full-time academia a try. He joined Arizona State University as an assistant professor, primarily teaching information systems, analytics and supply chain management. While at ASU, Demirkan co-edited two seminal research books in the emerging fields of service science and systems, and its industry-based implementation.

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In 2013, he moved to the University of Washington-Tacoma as an associate professor of service innovation and business analytics, tasked with expanding research and education programs in business analytics. He eventually became founding director of the Milgard School’s Center for Business Analytics and Master of Science Business Analytics, and the founding assistant dean of the Analytics Innovations Hub.

So far, he has published five books and more than 170 research articles. In 2021, Demirkan’s work and community building were recognized by the university with an award for both Distinguished Research and Community Engagement.

Throughout this academic stretch of his career, Demirkan maintained strong links with industry, developing data science, engineering and smart analytics solutions for dozens of leading companies, including IBM, GE, Cisco, HP, Intel, Bank of America, and Mayo Clinic. With AWS Academy Educator Accreditation, he still teaches a data analytics course at the University of Washington-Tacoma on weekends, guiding his students in developing AI and data-analytics-based solutions to novel business problems.

Researching “big problems”

So how did he end up full-time at Amazon?

“I’d come close to one of the highest positions in the academic career path,” says Demirkan. “The next step would have been to become a dean somewhere. But I didn’t want to be dean for near term: I prefer working on research for big problems.”

So, after almost two decades in academia, he decided to take a sabbatical. However, his restless nature meant he couldn’t be idle, so Demirkan applied to become an Amazon Scholar, a flexible program designed for academics who want to tackle large-scale technical challenges.

His plan: work one day a week during his sabbatical. During the interview with Amazon, however, it became clear that some of Amazon’s big business challenges dovetailed with Demirkan’s skillset so strongly — and offered him the opportunity to make such a big impact — that he decided to join the company full time by taking a leave from UW.

“My wife was like, ‘This is not a sabbatical!’”, recalls Demirkan.

The power of demand prediction

In his new Seattle-based role, Demirkan has two primary areas of business focus. The first is in making sales demand predictions for most Amazon devices. His team produces sales predictions for the majority of device types, globally, in which Amazon has a presence, and for every day from now until a year in the future.

To do this, Demirkan’s team ingests device sales data to train machine learning algorithms to generate increasingly accurate sales forecasts. Specifically, the team is employing advanced time-series forecasting methods, such as Random Forest, XGBoost, and Ridge Regression.

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“Based on that predicted demand, another Amazon team ships just the right amount of product to the right locations, so it’s where it needs to be just as a customer chooses to make that purchase,” says Demirkan. “Basically, our goal is to get the right amount of devices to the right place at the right time to better meet our customers’ needs. That's our goal.”

The forecasts Demirkan’s team creates do more than mere prediction. The team can also, for example, make projections for sales based on varying promotional prices.

“How many customers in London are going to buy a Kindle on April 21, if the price is x? The forecast assists our executive management teams to make decisions around how many units to manufacture, how many to ship, and when to ship,” says Demirkan.

By providing the company’s supply chain with increasingly accurate demand forecasts, Amazon simultaneously reduces delivery times and supply chain costs, helping the company keep prices low, while increasing customer responsiveness.

Demirkan is also developing a comprehensive, science-driven forecasting model called “Intelligent Demand Plan”. It will combine a wider range of inputs, including product cannibalization, macro-economic factors, traffic, social media and lots more, to sense demand and customer preferences with greater nuance, and to gain early insight into emerging market trends.

Automation and optimization

Demirkan’s second area of business focus: process and task automation and optimization, which utilizes his expertise in AI, process engineering and supply chain management. He and his team are analyzing the forecasting processes in Amazon’s devices group, identifying opportunities for improvement.

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“Amazon is one of the fastest-moving companies I have ever seen,” he says. “Everything is about providing the best possible service to customers, and innovation is happening so quickly here that processes designed as recently as six months ago may no longer be optimal.”

This is one of his research passions: machine-assisted cognition, also known as intelligence augmentation with artificial intelligence.

“Computers are already our assistants today, of course. I want to take these computing technologies to a more advanced level, using machine learning to, for example, train computers to teach themselves to provide me with what I need to know to make better business decisions,” Demirkan explains. “By making processes more automated, efficient, and error proof, we humans have time to do more value-added tasks.”

Breaking research silos

Demirkan said he expects his team to grow in the next six months. “I have applied scientists, research scientists, and data scientists. It’s one of many fast-growing teams at Amazon,” he says.

Demirkan’s transdisciplinary expertise — that combination of deep research knowledge and broad applications experience — is something he will be infusing into his team’s culture. Many education systems, he argues, with their tendencies to silo students in particular domains of expertise, are producing a generation of people who can find it hard to adapt to the wider commercial world.

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“With the digital transformation of companies in every industry, the coming generations of employees need to be more ‘T-shaped’ — innovators with not only a depth of expertise but also a broad, integrated understanding of other disciplines, such as management, engineering, and social sciences,” he says.

Last year, Demirkan’s insights into the changing nature of the high-tech workplace were sought by the US Department of Labor, Employment and Training Administration, when he was invited as an occupation expert to provide guidance on the definition of the occupation Operations Research Analysts.

“This role is about the combination of operations management, IT, data science, and machine learning — a very multidisciplinary, new occupation,” says Demirkan. “I'm hoping that in the future, academia will have more programs geared to preparing people for these crucial kinds of roles.”

With new roles in mind, how has Demirkan enjoyed his work with Amazon?

“A friend of mine said to me: ‘Haluk, you are going 35 miles an hour in academia, and now you are switching to 200 miles an hour?!’,” he says. “I love being a professor and making a difference in students’ lives, but I am relishing being back in industry because in 19 years, things have changed. I’m absorbing so much, and I can take this updated knowledge back to my classes when I teach on the weekend.”

From his professional perspective, Demirkan sees more clearly than most the gaps between academic education and the expectations of professional workplaces. “Globally, we have an ongoing mismatch problem. With my experience with Amazon, I can do my bit to close this gap,” he says.

Giving back

Doing his bit is central to Demirkan’s ethos. When Covid-19 struck in 2020, and hospitals all over the planet were suddenly critically overloaded, Demirkan was contacted by Virginia Mason Franciscan Health, one of the largest healthcare service and hospital providers in Washington state. They wanted his help to optimize their hospital bed allocations, among other things.

“We met online every week, trying to predict demand and capacity, which patients to move to other hospital facilities, looking at doctors’ and nurses’ scheduling — everything.” The urgency of the situation meant Demirkan was more than a volunteer advisor. “I was writing machine learning scripts, literally writing the code myself, to exploit the data quickly being gathered by the hospitals. I was proud to be involved in that work, because it was the first time I was able to make that sort of critical difference in people's lives.”

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In terms of making a difference at Amazon, Demirkan says: “My goal is to build an ecosystem in which technology is doing the labor-intensive tasks, freeing my team to do more smart work and value-added tasks. That's my idea of success.” That, and taking Amazon’s device-sales forecasting to a whole new level. “I want my team forecasting at a comprehensive, granular level,” says Demirkan. “I want to say with unprecedented accuracy that in this location, this device — in this color, size, and detail — will sell x units on this day.”

But Demirkan also sees additional potential in developing approaches that go beyond traditional forecasting. “I want to develop machine learning and data analytics that can discern what it is that customers really want and expect from Amazon devices; to generate insights powerful enough to actually impact the design decisions for brand new products and services.”

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Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!The Machine Learning Optimization (MLO) team develops algorithms and systems that improve the performance and delivery of Amazon’s Display Advertising campaigns and automates campaign management using machine learning techniques. The team develops and deploys machine learning solutions that drive ad selection, bidding, user response prediction, and automated campaign management. Customers are advertisers and publishers who do business with Amazon.We own the system for batch training of user response prediction models, while the ad serving engineering team owns the real-time model scoring component. This teams owns the system for automated management of advertising campaigns, which can dynamically adjust parameters such as budget, bid prices, and targeting to optimize for campaign performance.As an Applied Scientist on this team, you will: Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.Run A/B experiments, gather data, and perform statistical analysis.Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.Research new and innovative machine learning approaches.Recruit Applied Scientists to the team and provide mentorship.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE Advanced degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field.Published research work in academic conferences or industry circles.Experience in building large-scale machine-learning models and infra for online recommendation, ads ranking, personalization, or search, etc.Effective verbal and written communication skills with non-technical and technical audiences.Experience working with large real-world data sets and building scalable models from big data.Thinks strategically, but stays on top of tactical execution.Exhibits excellent business judgment; balances business, product, and technology very well.Experience in computational advertising.Key job responsibilitiesYou will work on the next generation of our real-time pricing systems. These systems are optimizing the price of every individual opportunity on behalf of Amazon Advertising advertisers. A day in the lifeConduct offline analysis of data to guide design decisions with the product teamConduct A/B test setup and analyze results to guide rollout, go to market or development priority decisionsSuggest and implement models to sophisticate the advertising products we offer to our customersAbout the teamThe Ranking team is responsible for real-time pricing decisions on the Amazon RTB (Real-Time Bidding) system
US, WA, Seattle
Job summaryAmazon Worldwide Advertising is one of Amazon's fastest growing and most profitable businesses. The Advertising Console Product and Technology team is a group of creative individuals whose vision is to make the Amazon Advertising Console (AAC) the most loved and used tool for all advertisers to market and grow their businesses, brands, and products to a global customer base. The AAC is a collection of federated applications that combine to form the face and brand of Amazon Advertising. ACPT owns the delivery of the software, processes, and tools that allow teams across Amazon to build, support and enhance applications and features that deliver a cohesive advertising experience to all advertisers worldwide. By using our development kit and reusable components, developers can rapidly build features that integrate seamlessly within the suite of advertiser products. We use survey research, data science, machine learning, experimentation, and predictive modeling to understand advertiser dynamics, drive platform optimization, support evidence-based decision making, and help to develop predictive, intelligent features. As the Applied Science Manager on this team, you will: Lead of team of scientists, business intelligence engineers, etc., on solving science problems with a high degree of complexity and ambiguity.Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects.Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization.Analyze historical data to identify trends and support optimal decision making.Apply statistical and machine learning knowledge to specific business problems and data.Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed.Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.Build decision-making models and propose effective solutions for the business problems you define.Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Job summaryAre you excited about joining a team of scientists building lasting solutions for Amazon customers from the ground up? Our team is using machine learning, and statistical methods to take Amazon’s unique customer obsession culture to another level by designing solutions that change customers behavior when it comes to product search, discovery, and purchase. In order to achieve this, we need scientists who will help us build advanced algorithms that deliver first-rate user experience during customers’ shopping journeys on Amazon, and subsequently make Amazon their default starting point for future shopping journeys. These algorithms will utilize advances in Natural Language Understanding, and Computer Vision to source and understand contents that customers trust, and furnish customers with these contents in a way that is precisely tailored to their individual needs at any stage of their shopping journey. Key job responsibilitiesWe are looking for an Applied Scientist to join our rapidly growing Seattle team. As an Applied Scientist, you are able to use a range of science methodologies in NLP/CV to solve challenging business problems when the solution is unclear. For example, you may lead the development of reinforcement learning models such as MAB to rank content to be shown to customers based on their queries. You have a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as SageMaker, S3, and EC2 with a variety of skillsets in shallow and deep learning ML models, particularly in NLP and CV. You will bring knowledge in many of these domains along with your own specialties and skilset.Major responsibilities:Use statistical and machine learning techniques to create scalable and lasting systems.Analyze and understand large amounts of Amazon’s historical business data for Recommender/Matching algorithmsDesign, develop and evaluate highly innovative models for NLP.Work closely with teams of scientists and software engineers to drive real-time model implementations and new feature creations.Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation.Research and implement novel machine learning and statistical approaches, including NLP and Computer VisionA day in the lifeIn this role, you’ll be utilizing your NLP or CV skills, and creative and critical problem-solving skills to drive new projects from ideation to implementation. Your science expertise will be leveraged to research and deliver often novel solutions to existing problems, explore emerging problems spaces, and create or organize knowledge around them. About the teamOur team puts a high value on your work and personal life happiness. 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 you. 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 establish your own harmony between your work and personal life.