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 … Continue reading Challenge Hiring Assumptions with Data
Photo by James Wheeler on Pexels.com One distinguishing feature of Automattic’s work culture is a team rotation, through which an individual can move from one team to another. A rotation can happen for a few reasons: to “try out” a new role and gain new skills, to backfill an understaffed team, or to cultivate cross-pollination and … Continue reading Advance your Career with Automattic Rotations
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: … Continue reading Looker NYC Meetup
In which Boris Gorelik shares his favorite talks and workshops from EuroSciPy 2018.