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

Related content

US, WA, Seattle
Economists in this role partner with business stakeholders to distill complex problems into testable economic questions and generate actionable insights. They collaborate with engineers and scientists to estimate models on large-scale data, design pilots, measure impact, and scale successful prototypes into improved policies and programs. They leverage AI tools to scale economic study for broader business impact. They communicate findings to business leaders, incorporate feedback, and deliver customer-centric solutions at scale.
US, NY, New York
Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Applied Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As an Applied Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals. 2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3. Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5. Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.
US, WA, Seattle
This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services. You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance. We are looking for an experienced Principal Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide. You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.
US, CA, San Diego
The Private Brands team is looking for a Research Scientist to join the team in building science solutions at scale. Our team applies Optimization, Machine Learning, Statistics, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business and develop Statistical Models and Algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Scientists, Engineers, and Economists. Key job responsibilities You will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable optimization solutions and ML models. This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and economists. As a Research Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. We are particularly interested in candidates with experience in Operations Research and predictive models and working with distributed systems. Academic and/or practical background in Operations Research, Machine Learning and Reinforcement Learning are particularly relevant for this position. To know more about Amazon science, Please visit https://www.amazon.science
US, CA, Palo Alto
Alexa for Shopping (previously Rufus) is seeking a Senior Manager, Applied Science to lead multidisciplinary teams of Applied Scientists and Machine Learning Engineers building next-generation conversational AI and multi-agent systems powering customer-facing experiences at scale. This leader will drive both scientific innovation and execution across large language models (LLMs), agent orchestration, retrieval and grounding systems, evaluation frameworks, and scalable AI infrastructure. The role requires a combination of deep technical judgment, organizational leadership, product and engineering partnership, and operational excellence. The ideal candidate has a strong track record of building high-performing science and engineering teams, translating ambiguous business problems into scalable AI solutions, and delivering measurable customer impact through applied machine learning and generative AI technologies. Key job responsibilities - Lead and grow teams of Applied Scientists and Machine Learning Engineers working on conversational AI and multi-agent orchestration systems. - Define and drive technical strategy for large-scale generative AI systems, including LLM routing, prompting, grounding, memory, tool use, personalization, and response optimization. - Partner closely with Product, Engineering, and Tech leadership to align AI investments with long-term business and customer goals. - Drive end-to-end delivery of production AI systems balancing quality, latency, scalability, safety, and operational reliability. - Establish scientific and engineering best practices across experimentation, evaluation, model iteration, and production deployment. - Lead roadmap prioritization and execution across research innovation and product delivery timelines. - Build scalable evaluation methodologies and quality frameworks for multilingual and global customer experiences. - Mentor and develop technical leaders across both science and engineering disciplines. - Foster a high-performance culture centered on customer obsession, innovation, operational excellence, and strong cross-functional collaboration.
US, NY, New York
We are seeking a Human-Robot Interaction (HRI) Applied Scientist to develop cutting-edge interactions that make robots feel alive, personal, and fun. In this role, you will focus on verbal and non-verbal conversational systems, social dynamics, memory, and long-term relationship formation between robots, their environments, and the people they interact with. Your contributions will be essential in advancing robotics by enabling expressive, socially intelligent, and trustworthy interactions between robots and humans. Key job responsibilities - Develop interactive systems that leverage large language models, multimodal inputs and outputs, reinforcement learning from human feedback, or other advanced techniques to achieve fluid, engaging, and socially appropriate robot behavior - Design and implement intelligent conversational systems that handle turn-taking, grounding, interruption, and incorporates context drawn from a robot's physical environment and shared history with a user - Integrate perceptual sensor streams including gaze, facial expression, gesture, posture, and more to understand social context and produce coherent, lifelike interactions. - Develop memory and personalization systems that allow robots to form lasting relationships with individual users, learn their environments, and adapt their behavior over weeks and months - Stay updated on advancements in HRI, NLP, multimodal AI, and cognitive and social science to apply cutting-edge techniques to robot interaction challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation
IN, KA, Bengaluru
Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies
IN, KA, Bengaluru
Do you want to join an innovative team applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about working with large-scale datasets and developing models that solve real-world fraud and risk challenges? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to help develop scalable machine learning solutions that safeguard millions of transactions every day. In this role, you will partner with senior scientists and engineers to translate business problems into data-driven solutions, build and evaluate models, and contribute to next-generation risk prevention systems, including applications of Generative AI and LLM technologies. Key job responsibilities Apply machine learning and statistical techniques to build and improve risk management models Analyze large-scale historical data to identify risk patterns and emerging trends Develop, validate, and deploy innovative models under the guidance of senior scientists Experiment with emerging technologies, including GenAI/LLMs, to enhance automation and risk evaluation Collaborate closely with software engineers to implement models in real-time production systems Partner with operations and business teams to improve risk policies and operational efficiency Build scalable, automated pipelines for data analysis, model training, and validation Monitor model performance and provide clear reporting on key risk and business metrics Research and prototype new modeling approaches to improve system performance
IN, KA, Bengaluru
Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies
IN, KA, Bengaluru
Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions