Eugene Yan
Eugene Yan is an applied scientist at Amazon, but he’s also known for his personal site where he covers topics like machine learning systems, data science methodology, dealing with imposter syndrome, and building data science teams.
Courtesy of Eugene Yan

Eugene Yan and the art of writing about science

Why the Amazon applied scientist takes the time to break down his work for readers.  

Eugene Yan’s career path has taken some unusual turns, but his motivation has always been the same: understanding people so he can help them. A policy analyst turned data scientist, Yan is now an applied scientist at Amazon using customer-behavior data to help recommend the best products. In the world of machine learning, however, he’s best known for the way he writes it all down. On his personal site, Yan covers a range of professional and technical topics like machine learning systems, data science methodology, dealing with imposter syndrome, and building data science teams.

Eugene Yan started eugeneyan.com in 2020, focusing on general machine learning and career content. Initially it was for personal development, but then people started reading, and now writing posts takes up the majority of his leisure time.

He started the site for personal development, but then people started reading, his network started expanding, and now writing posts takes up the majority of his leisure time. “It snowballed,” he said. “Writing helps me learn better. And when I share it online, it attracts like-minded readers and helps me make new friends. ”

Born in Singapore, Yan studied psychology at Singapore Management University. “I was curious about people, how they perceive and how they behave,” he said. His college research focused on how competition affects people differently — motivating some and intimidating others. After college, he joined the Singapore government as a policy analyst sifting through legal cases and trade agreements. But it wasn’t long before he began to miss crunching numbers and following the data on human behavior. “I began to envy my colleagues in commodities who relied on numbers and worked with spreadsheets,” Yan said.

He decided to try and make the switch to a data science position based on some familiarity with the subject from his undergraduate research days. He landed a position at IBM in 2013, and from there he moved to data science roles at Lazada, a Southeast Asian e-commerce site, and then UCARE.AI, a healthcare startup.

A desire to help

“In every change in my career, what drives me is helping people,” Yan noted.

At IBM, it was helping people find new roles. At Lazada, it involved helping people find products they need. At UCARE.AI, it entailed predicting chronic diseases and preventing high insurance payouts. “This brings me way more satisfaction than dollars and cents,” he explained.

While at Lazada, Yan decided he needed more training in the fundamentals and pursued a master’s in computer science from the Georgia Institute of Technology. He graduated in 2019, and then he and his wife began considering a move overseas. He applied for a position at Amazon, drawn to the company’s leadership principles and the ability to help customers read more. He relocated to Seattle to join Amazon in 2020.

While Amazon has several ways to help readers find books, from Amazon Book Review to Amazon Charts, Yan is part of a team developing the recommendation systems that power the widgets behind the Amazon Store’s personalized book suggestions. “Customers tell us what they like based on what they do,” he explained. “They browse for a specific book, a genre or a topic.” His team uses those signals to help surface additional books a reader might like. Ultimately, Yan and his team want to make reading easier.

Writing it all down

Early in his transition to data science, Yan started interviewing mentors for advice, some of whom were “rock star data scientists.” He asked what skills he should cultivate to be successful. The one skill a majority of mentors suggested was communication. The people he spoke with emphasized how communication becomes more and more important as you rise in the ranks. “I was like, ‘Are you kidding me?’ But more and more mentors said the same thing,” he recalls. “I thought, ‘This can’t be right, but I'm just going to try it.’”

Yan started practicing his writing, first publishing to a WordPress site. He wrote dozens of posts unnoticed, but then in 2020 created eugeneyan.com and started writing more general machine learning and career content. His writing began to gain an audience. Posts like “Unpopular opinion — Data scientists should be more end-to-end” received more than 500 likes on Twitter. One post on note-taking received 35,000 unique views in a single day. Feedback and praise began to pour in, and his “practice” website swelled into something much bigger.

For a brief period, Yan tried to sustain this level of social engagement. He wrote to please a mass audience and get clicks. “That quickly became unfulfilling,” he said. Now he focuses his writing on topics he wants to learn and aims for an audience of people he’d hope to be friends or colleagues with. “I might have fewer readers now since I’m choosing more technical topics, but these readers comment, disagree, and email me. Each comment and real relationship are worth more than 10,000 likes,” he said.

The many benefits of good writing

Yan's decision to become a better communicator and writer is especially valuable at Amazon. “The writing culture is rigorous at Amazon,” he said.

Amazon’s working backward method starts with an individual or team imagining the product or service is ready to launch. The individual or team’s first step is to draft a press release announcing the product’s availability, and explaining its significance. Moreover, meetings often start with participants reading a six-page document about the meeting’s topic before discussion begins.

Finding your voice and niche doesn’t happen overnight — you have to write and share your work. So just start somewhere, anywhere, and keep writing.
Eugene Yan

“I write as many documents as I code,” Yan said. Recently he received feedback that one of his design documents was easy to understand and clearly laid out everything the reader needed to know. In this way, his writing skills complement his design and machine learning skills. He also started a new site, Applying ML, which includes interviews with machine learning practitioners.

Yan is often asked by aspiring writers for advice on how they can improve their skills. The number one piece of direction he offers is to write for yourself — what do you want to learn and clarify your thinking on? — rather than social engagement. The second piece of advice: “just write.” The best way to figure out your niche and your audience is to simply put fingers to keyboard and start practicing, he said. Maybe after a dozen — or a few dozen — pieces you find your voice, what you want to write about, or what resonates best with the people reading along.

“If you never start writing, how will you know? Just like Blue Origin’s motto ‘Gradatim Ferociter’, which means ‘Step by step, ferociously’. Finding your voice and niche doesn’t happen overnight — you have to write and share your work,” he said. “So just start somewhere, anywhere, and keep writing.”

Research areas

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Amazon Fulfillment Technologies Amazon Fulfillment Technologies develops tech solutions that create network optimizations for all Amazon sites worldwide. We own network infrastructure and devices in all buildings and labor planning and scheduling technology for fulfillment centers in North America and Europe.
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Applied Scientist III Job Location: Seattle, Washington Job Number: AMZ10037670 Position Responsibilities: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. Position Requirements: Master’s degree or foreign equivalent degree in Computer Science, Machine Learning, Engineering, or a related field and two years of research or work experience in the job offered, or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation. Employer will accept a Bachelor’s degree or foreign equivalent degree in Computer Science, Machine Learning, Engineering, or a related field and five years of progressive post-baccalaureate research or work experience in the job offered or a related occupation as equivalent to the Master’s degree and two years of research or work experience. Must have one year of research or work experience in the following skill(s): (1) programming in Java, C++, Python, or equivalent programming language; and (2) conducting the analysis and development of various supervised and unsupervised machine learning models for moderately complex projects in business, science, or engineering. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $167,100/year to $226,100/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. #0000