Emine Yilmaz: An Amazon Scholar advancing the state of the art in voice shopping

Scientist leads team in London focused on improving voice-shopping experiences with Alexa.

Emine Yilmaz is a computer science professor at the University College London (UCL) and a faculty fellow at the Alan Turing Institute. Her research interests include information retrieval and natural language processing. Yilmaz is the recipient of several honors and awards in her career, including a 2018 Bloomberg Data Science Research grant for her work on building task-oriented systems, and a 2015 British Computer Society Information Retrieval Specialist Group Karen Spärck Jones Award for her research contributions in the field of information retrieval.

Emine Yilmaz, an Amazon scientist, is pictured sitting at a table with an open laptop in front of her
Emine Yilmaz, a computer science professor at the University College London, a faculty fellow at the Alan Turing Institute, and an Amazon Scholar, is shown speaking at an Amazon Research Day event. At Amazon, Yilmaz works within the Alexa Shopping Research and Science organization.
Emine Yilmaz

Yilmaz is also an Amazon Scholar, a select group of academics who work on large-scale technical challenges for Amazon while continuing to teach and conduct research at their universities. At Amazon, Yilmaz is leading a research team based in London that’s responsible for improving the Alexa voice shopping experiences.

Given the nascency of the field—the first Echo speaker was launched six years ago—customer satisfaction in voice shopping is an open area of research. Yilmaz is uniquely positioned to drive meaningful innovations in the field. She has been involved with advancing research in modeling user behavior and predicting user satisfaction for her entire career. One example: a recent paper that Yilmaz coauthored with Manisha Verma, “Search Costs vs User Satisfaction on Mobile”, in which they studied the impact of user actions, such as inputting search queries, reading snippets, or scrolling through a search engine result page, on customer satisfaction.

Amazon Science spoke to Yilmaz about her career, her work at Amazon, and why she thinks academics will enjoy working at Amazon.

Q. What drew you to your research interests in information retrieval and natural language processing?

My interest in machine learning was sparked during my undergraduate program. As part of an assignment for a computer science class, we had to implement a machine learning algorithm that would learn to put a number of small rectangles into the smallest rectangle shape possible. I found the concept of a computer being trained to perform tasks fascinating, and decided to pursue a master’s degree in machine learning.

When I began my PhD, web search technology was newly emerging. I was intrigued by how search engines were able to retrieve results relevant to a query in a near-instantaneous manner. There were, and there still are, many open problems in the domain, and nearly all of them can be tackled using principles from machine learning. I thus decided to choose as my area of research machine learning applied to information retrieval (the computer science discipline behind search) and natural language processing.

Q. What are you working on at Amazon?

At Amazon, I’m part of the Alexa Shopping Research and Science organization headed by Yoelle Maarek. Customers interact with Alexa for a variety of shopping-related tasks—from product research to actual purchases. My team’s goal is to continually improve Alexa so that she is able to help customers no matter where they are in their shopping journey.

Q. What are some of the research problems you’re tackling at Amazon?

Annotating customer interactions with pertinent data is critical to training Alexa to get better over time. However, with billions of interactions every week, it isn’t feasible to annotate even a small percentage of those interactions manually.

Further complicating matters is the growing number of experiences that Alexa-enabled devices provide. To give just a few examples, Alexa is available on a wide range of smart speakers, tablets, smartphones, and an ever-increasing array of smart home devices. A successful customer interaction on an Echo device (adding an item to one’s shopping list) can be quite different from that on a tablet (clicking and zooming in on an image).

My team’s goal is to continually improve Alexa so that she is able to help customers no matter where they are in their shopping journey.
Emine Yilmaz, Amazon Scholar

My team applies state-of-the-art natural language processing and machine learning models to predict customer satisfaction across all of these diverse experiences. To do this, our models look at implicit criteria to evaluate whether Alexa helped customers meet their goals. These criteria include search query reformulations, how much time customers spend interacting with search results, or even whether they zoomed in to study a product image in greater detail. By studying patterns in user interactions, we are able to drive improvements to the Alexa voice shopping experience at scale.

Q. How do you see the nascent field of voice shopping evolving?

These are early days for voice shopping. That’s one of the primary reasons this is a fascinating area to be involved with. Similar to mobile phones today, I believe that intelligent voice assistants will become an embedded part of our lives. Shopping using our voice is a much more frictionless experience. Most of us speak faster than we type. With voice agents, you don’t have to take your phone out, unlock it, type out a search term and take a series of steps to complete your request. To give just one example, today you see residents of senior living centers, who would ordinarily struggle using computers, but who are using Alexa to stay connected to friends, family, and the world during COVID-19. Intelligent voice agents are going to be an integral part of our day-to-day lives. I’m really excited to be at Amazon, and have the opportunity to shape the future in how people use voice to conduct research on, and buy products.

Q. How did you come to join the Amazon Scholars program?

I received a call from an Amazon recruiter in 2019, who told me about the Amazon Scholars program. This seemed really intriguing. Indeed, to say that the entire ecosystem around Alexa is cutting edge would be a massive understatement. I was excited at the opportunity to find out more about the kind of problems the team was working on, and to see if I could contribute to their research.

I was also impressed by the investments Amazon has been making in research. At the time, Amazon had recently opened the Cambridge Development Center. They were actively hiring great talent to further innovation in multiple AI disciplines.

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Lastly, I was drawn to working with scientists I’ve always held in high regard —be it Michael Jordan, Thorsten Joachims or Eugene Agichtein. Some of the world’s leading researchers are working as Scholars at Amazon. And given my prior work and research area interests, I was particularly interested in the work of Yoelle Maarek’s team.

Q. How do you balance your work between Amazon and University College London?

At Alexa Shopping, I’m constantly encouraged to write and publish papers at the top research conferences, both within Amazon and at my university. It certainly helps that my research areas in academia and at Amazon are distinct yet aligned. To give just one example, as part of my academic work, I recently coauthored a paper, From Stances' Imbalance to Their Hierarchical Representation and Detection , that was presented at The Web Conference in 2019. In the paper, we proposed a new approach to detecting fake news—news that purports to be factual, but which contains misstatements of fact with intention to arouse passions, attract viewership, or simply deceive. On one hand, the paper is sufficiently distinct from shopping that I can differentiate between my work in academia and at Amazon. On the other hand, the research outlined in the paper can help me invent methods towards ensuring that sellers’ descriptions on product listings are accurate.

Q. In your mind, why would academics enjoy working at Amazon?

First, the caliber of talent at Amazon is very high. I attribute this to the hiring process based on a set of Leadership Principles. The hiring process is concrete and structured, and ensures that we are always meeting a high bar when it comes to recruitment. Because the bar for hiring is so high, I’m constantly learning from my managers, from my peers, and from people who report to me.

I also think academics will readily appreciate Amazon’s “customer obsession”, one of our key Leadership Principles. In my mind, this is the primary reason academics should consider working at the company. Throughout my career, when I’ve thought about research, I’ve also thought about the end application. At Amazon, you have the opportunity to have a positive impact on the lives of millions of people. Staying focused on the customer and working a solution backward makes our research a lot more fulfilling. It also keeps you grounded, and prevents you from drifting into irrelevance, both in academia and within the industry.


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Candidates should be comfortable with nested modeling to assess the impact that variables at the team- and organizational-level has on individual-level outcomes. Experience with applying machine learning to develop classification models as well as forecasting models is a plus, but not essential. There is no opportunity in this role to outsource work to others, so candidates must be comfortable taking on a several activities (i.e., identifying data sources, attaining data via SQL, shaping and analyzing the data, and writing up and communicating the results).The successful candidate will also have a demonstrated track record of:· Knowing Your Customers. Understand their priorities, what they are saying, and more importantly, what they aren’t. Deliver and drive value for them.· Innovation. Appreciate what exists, identify why it works. Then rebuild it or build something totally different that derives even MORE value and a better customer experience.· Judgment. Take different perspectives and business needs, develop a solution that works, move the ball forward. Be able to support your opinions with sound reasoning grounded in the business.· Systems Thinker. Understand all the connections and integrations points through the entire talent management lifecycle.· Thinking big. How do we build it bigger, better, faster? What aren’t we thinking of?· Research driven. We seek to be the most scientific HR organization in the world. We form hypotheses about the best talent management techniques and then set out to prove or disprove them with experiments and careful data collection.· Prioritization. There will be a constant flow of work, both tactical and strategic. Determine what gets done first and why, while managing a plan for what to do with everything else.· Building relationships. Partner with AWS HR COE teams, HR Business Partners, Business Leaders, and corporate COE teams. Share best practices, partner on solutions, and move the organizations forward together.· Consulting. Utilize past experience, deep knowledge of processes and policies, and knowing what’s going on across our organization to create custom solutions and localize as needed.· Communicating. Share your ideas, listen to others, follow-up, and follow-up again. Use the language of the business and avoid “consultant speak”.· Resourcefulness. If you don’t know it, that’s ok. But you should know where to go for the answer or how to find out.· Bias for Action and dealing with ambiguity. Sometimes it is not clear how we are going to get there. Can you help carve a path? How fast can you do it? What are the tradeoffs? Take risks and be willing to try new things, fail fast and iterate.KEY RESPONSIBILITIES:· Manage full life cycle of large-scale research programs (i.e., develop strategy, gather requirements, manage, and execute).· Conduct experiments, analytics, and compute behavioral process models for a deep dive understanding of systemic and individual level drivers; combine quantitative and qualitative data to inform research.· Conduct organizational analysis to uncover talent and org effectiveness insights and build talent and organizational effectiveness solutions (both scrappy or long-term) and mechanisms with the objective to help the business increase talent leverage and improve organizational speed and execution.· Partner closely with our Analytics, Employee Survey (“Connections”), and Diversity and Inclusion teams to identify opportunities for integration points and address specific talent needs leveraging talent data and themes.· Partner with AWS Analytics team to automate organizational health reporting and innovative tools to uncover organizational insights to drive strategic talent and organizational discussions.· Measure and assess the impact of tools and resources introduced to the organization. Continually evaluate solutions for quality, business impact, scalability and sustainability. Conduct post launch evaluations to understand successes and improvement opportunities while establishing methods for sustained adoption.· Evaluate research initiatives to provide bottom line value, ROI, and incremental improvements over timeIf you have an entrepreneurial spirit, know how to deliver, are deeply technical, highly innovative and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you.