Hi, I'm Eugene Yan. I design, build, and operate machine learning systems that serve customers at scale. I also write and speak about ML systems, engineering, and career.
I'm currently a Senior Applied Scientist at Amazon, shipping ML and recommender systems to help customers read more. Here, I built real-time retrieval, bandit-based ranking, and recsys in search. I also shared our work at a RecSys keynote.
Previously, I led machine learning at Lazada (acquired by Alibaba in 2016). I also built the product ranking system (conversion & revenue up 5-20%), smart push-notifications (CTR & add-to-cart up 10%), and automated product & review classification (cost down 90%). Hypergrowth's fun!
As an early hire in a healthtech Series A, I led the team to ship an ML system for Southeast Asia's largest healthcare provider. At IBM, I built job forecasts & recommendations.
Outside of work, I share the ghost knowledge of applying ML via curated papers, guides, and practitioner interviews, and host a monthly meetup on ML in industry. I also angel invest in early-stage data/ML, infra, and devtool startups.
2020-now: Senior Applied Scientist @ Amazon
(ML, RecSys, LLMs)
2018-2019: ML Lead @ Healthtech Series A (Disease Detection, Cost Estimation)
2017-2018: VP, Data Science @ Lazada (Alibaba) (E-Commerce ML Systems)
2015-2017: Data Scientist @ Lazada (Alibaba) (Ranking, ML Automation, MLOps)
2013-2015: Data Scientist @ IBM (Workforce Analytics, Fraud Detection)
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I read everything but receive too much to respond to all of it.
Eugene possesses a rare blend of technical knowledge, business acumen, capacity to learn, and passion for doing the right thing for the customer/business. Throughout his time with Lazada he proactively sought feedback and improved himself, going from strength to strength in delivery, team development and individual mentoring. It would be a pleasure and honor to work with him again.
Klemen Drole, Finance & Tech at Vestiaire Collective
Eugene gave a very inspirational keynote at the OLX Group Product and Tech 2019 conference in Amsterdam and absolutely nailed it! Given his hands-on experience as a data scientist, he was able to share the nitty gritty details with a technical audience. On the other hand, he was able to take a helicopter view explaining trends and developments. Thanks once again!
Merlin De Graaf, Strategy at Vinted
We were having a project review and Eugene shared that he had resolved an unforeseen issue by writing a Python program. I noted that I didn’t know he could code Python. He said he didn’t, but that was before he’d run into the issue. He found that Python was the best way to solve the problem, so he taught himself Python. I share this story because this captures the essence of Eugene. When a barrier is placed in front of him, he doesn’t stop, he pushes through.
Karen Midkiff, Manager Workforce Analytics at IBM
Eugene is an excellent leader whom I admire, respect, and am grateful to work with. Though he delivered creative and high-impact projects, he remains open-minded and humble. His philosophies of hiring and leadership played a crucial role in building Lazada's Data Science team as one of the strongest in SEA. He is the effective leader I’d choose to follow because he supports, protects, promotes the team's efforts, inspires, challenges, and encourages the team to think bigger.
Quy Nguyen, Principal Data Scientist at NE Digital
Eugene is a data scientist who finds the best way to solve business problems simply. He is leader who socialized data science throughout Lazada and enabled BUs to incorporate data products throughout Lazada. He is a mentor who cares deeply about his team on a personal and professional level, especially about the team's growth and well-being. I was lucky to work with Eugene, learn by observing his conduct and work ethics, and by being mentored by him.
Michal Polanowski, Data Science Head at GoJek
Eugene impressed me since the POC where he understood the requirements well and could articulate the solution clearly to us. During the production phase, Eugene directly observed users in day to day ops, and was open to our feedback and that resulted in an elegant solution which benefit our organization. Eugene is a very rare breed of high caliber data scientists which have deep/complex technical skill and yet able to communicate clearly to users and business level.
Harry Chan, AVP at Parkway Pantai Healthcare Group
Despite being in a technical role, Eugene is an excellent communicator, which is an important skill as a manager. He can distill the essence our technical work to convince stakeholders of its value. He also places strong emphasis on setting out a clear road map and prioritisation so everyone can work at their best. Eugene takes a long term view when designing engineering systems to ensure that complexity and maintenance efforts are minimised when in production.
YuXuan Tay, Machine Learning Engineer at Meta
Eugene quickly grew into the Lead Data Scientist position through his consistent delivery of results (e.g., successful POCs and production systems), and communication and leadership abilities. With his business acumen and ability to empathize with clients, he solved their business needs and delivered practical solutions. He proactively improved himself via constant learning and seeking feedback, and mentored the team effectively into full-fledged data scientists.
Zhao Chuxin, Engineering Director at Minden.AI
It was my privilege to work with Eugene; he showed what a 10x Data Scientist can do, ranging from understanding the data and business problem, applying the right techniques, to writing production code. Eugene played an important role in hiring and fostering our team culture by hosting knowledge sharing sessions and team bonding activities. With his contribution, we delivered 4 cost predictor models for the second-largest hospital chain in the world within 3 months.
Nguyen Lam Phuc, Lead Site Reliability Engineer at Vortexa
Eugene embodies the spirit of servant leadership. He leads by example and is always supportive in guiding me to achieve my goals, often going the extra mile to ensure my learning needs are met. He provided timely and constructive feedback which helped improve the quality and efficiency of my workflow. He gives genuine advice, impacting the decisions I made in the first year of my career. His enthusiasm and deep understanding of data science is contagious and will always be an inspiration to me in my pursuit of knowledge.
Lim Wen Qing, Data Scientist at Twitter
Bio
Eugene Yan designs, builds, and operates machine learning systems that serve customers at scale. He's currently a Senior Applied Scientist at Amazon. Previously, he led machine learning at Lazada (acquired by Alibaba) and a Healthtech Series A. He writes & speaks about ML, recommendation, and LLM systems at eugeneyan.com and ApplyingML.com.
Images: 960 x 960, 738 x 738, 200 x 200
FAQ
Why do you write?
I enjoy sharing my experiences and what I’ve learned, so others can avoid my mistakes and build on my lessons. I’ve been fortunate to work in awesome teams to solve challenging data & ML problems and ship to customers worldwide. Here, I hope to share some lessons and perspectives, with a pragmatic and product slant.
Also, as I continue to explore and learn, writing helps me to learn better. And when I share my thoughts and writing online, they attract like-minded people with whom I can discuss with and learn from. Read something that you would like to share, ask, or discuss about? Tweet me at @eugeneyan or reach out via email.
What do you write about?
I’ll write about what I’ve learned or thought about. It’s usually related to the topics of data science, ML in production, or career. Here’s a word cloud based on the 55 posts I wrote in 2020. We can see common themes in (i) data & ML, (ii) problem & product & user & people (iii) writing & coding & learning.
Sometimes, I also write to answer questions I get from readers. This includes questions about my productivity habits, why read papers, the importance of writing for tech roles, and the difference between data/ML roles.
There will also be writing that might not be related to data science or ML. This includes topics on Commando, Soldier, Police, Beginner’s mind, and the 85% rule. Nonetheless, I hope you’ll also find them valuable.
Truth be told, I’m still figuring it out. Don’t be surprised if I write about completely different topics in future. If you want to keep in touch, subscribe to my newsletter.
Who do you write for?
I mainly write for two individuals and a group.
The first person I write for is myself. I try to write things that, when I revisit in a year or two, are (still) interesting; hopefully, this makes it useful for others too.
The second person I write for is my wife. She's always the first reader of my drafts (at least, until she gets sick of it). If she can understand what I write, especially on some of the more technical topics, mission accomplished.
The group of people I write for is my previous, current, and future teams. I pen my views on machine learning in production, data science & agile, and end-to-end data science, etc. This way, when the need arises, I've thought through them.