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Challenge Hiring Assumptions with Data
We use experiments to model reality (sometimes to create alternative realities as in A/B experiments), to understand reality, and ultimately, to make decisions moving us ever closer to our goals. Improving iteratively, we learn not only from successful experiments but also from failed attempts. Experiments are important because they provide us with measurements. And measurements…
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Join us at Tumblr!
During an interview, a candidate recently asked me why, after more than five years, I still work at Automattic. Why? I like the people I work with, and they alone are a good reason to stay. Being globally distributed means I get to work with colleagues from various backgrounds. This helps me understand first‑hand what’s happening…
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Looker NYC Meetup
Like any company, Automattic is constantly on a journey to get better: sometimes we have the good fortune of finding improvement in leaps and bounds, but most of the time, we move slowly, we make small changes, finding iterative wins and moving down the to‑do list. I think probably this is how most progress happens:…
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This Week in Data Reading: Experimentation, Tech and the Humanities, and Eliminating Bias in Testing
This week, Carly, Demet, and Charles bring you some interesting material on tech and the humanities, experimentation culture, and eliminating bias in testing.
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This Week In Data Reading: MOOCs, Collusion in Artificial Intelligence, and Fake News
This week, Boris, Xiao, and Carly share recent reads about MOOCs, collusion in AI pricing, and generating fake news with artificial intelligence.
Data for Breakfast 