Etsy’s Data Science & Machine Learning (DSML) organization is comprised of talented engineers and scientists with diverse backgrounds.
Together, we tackle challenging problems in Machine Learning, Statistics, and Large-Scale Systems, to improve the lives of millions of buyers and sellers on Etsy around the world.
Publications
The DSML org at Etsy stays involved in the research community in order to stay ahead of the technology curve. Check out our recent papers to see what we’ve been working on. A full list of publications can found on our publications page.
Projects
Our work on the DSML teams span across many product applications including Recommendations, Personalization, Search, Computer Vision, Advertising, and Marketplace Economics.
Learn more about our projects below. (Coming Soon!)
Careers
We are currently hiring!
If you’re interested in working with us, check out our current openings in DSML below. To see more job openings at Etsy, visit our Careers Page, in the Data Science & Machine Learning Sub Department under the Engineering.
Applied Scientist, Computer Vision (SF, Open to Remote)
Applied Scientist, Applied Machine Learning (BK or SF)
Senior Data Scientist, Experimentation (BK or Open to Remote)
Senior Applied Scientist, Trust & Safety (BK, SF, or Open to Remote)
It is common in the internet industry to use offline-developed algorithms to power online products that contribute to the success of a business. Offline-developed algorithms are guided by offline evaluation metrics, which are often different from online business key performance indicators (KPIs). To maximize business KPIs, it is important to pick a north star among all available offline evaluation metrics. By noting that online products can be measured by online evaluation metrics, the online counterparts of offline evaluation metrics, we decompose the problem into two parts. As the offline A/B test literature works out the first part: counterfactual estimators of offline evaluation metrics that move the same way as their online counterparts, we focus on the second part: causal effects of online evaluation metrics on business KPIs.