Amazon’s massive makes it possible to adapt to the customer’s shopping experience in countless ways. It’s no surprise when the Amazon store expects you to come back to buy the goodies your dog loves and you see the setting on the front of the website.
But there may also be similar brands worth considering for your puppy. You also need to be because of more face wash, an item in your wish list is on dirty, and more options match the jeans you were looking for the other day.
Ren Zhang’s team uses science to present customers with the most relevant offers possible. As Director of Research Science for Personalization Strategic Initiatives, Buy Again, Amazon Fresh, Amazon Business, Pets and Beauty, included Zhang Deep Learning for tailor -made, real -time recommendations to customers.
“Our goal is to understand the customer’s intention at that moment and provide under experience and products,” she says. “The complexity we solve and the influence we have makes our team scientists.”
Predicts the needs of the customer
As every shopper knows what you have intent to do when you come to a store and what you News Do is often two different things. Did you buy dog treatments because you went out to do it, or to have seen those who saw them reminded you of?
Understanding customers’ preferences can be challenging in a noisy environment where customers can have a number of potential factors that motivate a decial. For example, if a product’s location affects the decision, it is a case of bias caused by position. Some items are more likely to be selected by customers because they are located in more visible places. This is just one of many challenges Zhang’s team is trying to solve.
“There are all kinds of interesting science problems in a very noisy environment,” says Zhang. “How do you design an experiment with so much going on and really learns the right thing?”
Zhang and her team have published research on how to refine recommendations in a field of probable choices – e.g. Toy Story Recommended because it’s up to you just because it’s a wildly popular movie? In the paper “does not recommend the obvious: estimation of probability”, they demonstrate how popularity -stricken measurements are better suited to personalized recommendations, they would directly measure a system’s ability to identify specially for user overrun.
Another challenge is to bring transparency to the recommender. While dearing algorithms surpass traditional methods, they do not, by default, give any justification for the conclusions they reach.
“Typically, customers are more likely to engage in a recommendation if they know they get it. But we’re talking about millions of items coming with an answer. How do you decipher that for customers?” She says. “The complexity of the methodology, the massive data and the speredd -responds make the problem extremely challenging.”
Zhang and her team collaborate with Amazon -Science as Iain Murray on solutions to such problems. A professor of machine learning and inference at the University of Edinburgh, Murray contributes to the team’s research and brings his own ideas.
“There is a lot of brainstorming sessions,” Zhang says of the relationship with Murray. “It not only helps us to move on, but it is also a great talent magnet. Scientists are attractive to a society that promotes innovation and research in science.”
Zhang ensures that 20 to 30% of her team’s time goes to Exploratorry Research, which they believe is the future – work that in the short term may not have a direct business impact.
“I have a much higher tolerance for failure when it comes to this kind of research,” she says. “Scientists need that kind of freedom to explore, to feel excited. That kind of energy is where you need to have a high -performance team and have the right culture.”
Focused on business impact
Zhang joined Amazon in July 2021 after a series of leading data science roles in the financial world of companies, including Prudential and American Express. A love for math led her to study the probability and statistics at Peking University in China, where she served her bachelor’s degree and finally to serve her doctorate in statistics from the University of Pennsylvania’s Wharton School.
I love creating business impact and I could see Amazon as an innovation power center that would be a fabulous place for me to do it. The end is extremely talented people in my career, but the anxiety bar at Amazon is so high.
Her adviser in Wharon, the late Lawrence Brown, was a learned through and through, and her studies were referred to Akademia. But after some business internships and conversations with the formation of a friend, Zhang realized that she would solve practical problems.
“It took me who was some courage to go to him and say, ‘Hi, Larry, I don’t think I want to be a professor, I have to go into the industry,'” she remembers.
Her first job was at American Express, where she says she “grew up, career -wise.” Over the course of almost 13 years, she took on roles in various departments, from business loans to membership payments to risk and information management. When she was the head of the Data Scientist for BMO Financial, an Amazon re -recruiter reached out. She recognized the opportunity to move to the technology sector, which had long fascinated her.
“I love to drive business and I could see Amazon as an innovation power center that would become a fabulous place for me to do it,” she says, adding that she has been impressed with Amazon’s talent. “I am extremely talented people in my career, but the change line at Amazon is so high.”
Zhang took over as director of Data Science for Worldwide Selling Partner Development and helped Amazon Sellers extract insight as how customers found their products and ways to build customer loyalty. She switched to her current role in personalization in the spring of 2022, fascinated by the ability to experience and ultimately see direct benefits for customers.
Any scientist who wants to work in the industry must have a practical mindset, according to Zhang. “You need to be able to use data science to solve a business problem,” she says. “The hardest talent to find, typically, is a talent that can bridge the fields.”
Zhang works herself to make sure she knows the business and technical side of any scientific topic at Amazon. She points to Ash Guepta, which she worked early in her career when he was Chief Risk Officer at American Express, as a role model. Even as a senior manager, Gupta would often go into depth and challenge her staff in various areas such as likes, processes and police. He had a deep knowledge of the risk management area while driving the vision at the same time.
It is a level of discipline Zhang considers important to anyone who wants to promote good science but also deliver results.
“No matter what level of leader you are,” she says, “you should never be waived from what is going on in the field beyond your own area. Science will not be able to deliver influence without an appealing design of customer experience and robust engineering system.”