Great article. Designing a/b systems always seems (relatively) simple at the start, but in my experience there are 1,000 things you don't think of until you have massive amounts of worthless results. Add this to the list of things to watch out for.
I think using them together is crucial. Qualitative research gives you insight into what you should look for through quantitative research. It's important to start with some basic qualitative research (talking to a few customers) and then scale it through quantitative methods (analyzing logs).
I like the part about arming others to help you. I've found this to be critical. The more your job specs reflect the excitement of working at your company, the more other people can evangelize that for you.
Shutterstock - New York, San Francisco, Remote, Visa
We're hiring all sorts of software engineers and data scientists. We've got some pretty fun problems -- image search, video search, storage scalability, tons of behavioral data to mine -- and an awesome team. We prefer folks to work in one of our offices, but are always willing to consider remote superstars.
Shutterstock - New York, San Francisco, Berlin, Remote, Visa
We're hiring all sorts of software engineers and data scientists. We've got some pretty fun problems -- image search, video search, storage scalability, tons of behavioral data to mine -- and an awesome team. We prefer folks to work in one of our offices, but are always willing to consider remote superstars.
Shutterstock - New York, San Francisco, Berlin, Remote
We're hiring all sorts of software engineers and data scientists. We've got some pretty fun problems -- image search, video search, storage scalability, tons of behavioral data to mine -- and an awesome team. We prefer folks to work in one of our offices, but are always willing to consider remote superstars.
Shutterstock - New York, San Francisco, Berlin, Remote
We're hiring all sorts of software engineers and data scientists. We've got some pretty fun problems -- image search, video search, storage scalability -- and an awesome team.
Great questions! Each team has a specific domain -- we have a customer experience team, a contributor team, a search team, etc. Each team also has one or more stakeholders -- business folks that help set the high-level direction of the team. The teams work with their stakeholders (some of whom are super hands-on, others aren't) to pick 2-3 projects per quarter to focus on. We try to talk in terms of the problems each team should solve, and let the team itself work out the best solution.
It has taken us years to refine our process, and it's been a great journey. I'm happy to chat more about it with anyone -- hit me up at dan at shutterstock.com
We have about 14 teams. The topic of management is a great one. We've shifted our approach over the years. That story might make a good blog article in itself. We've ended up with what's sometimes called a "matrix" structure, where people's day-to-day activities are based on what the team is doing, but each role (dev, tester, UX) reports into a manager (who's usually not on the team) with expertise in that domain. We've mostly avoided middle managers who only manage -- nearly all our managers are also on teams and, if they're devs, code. But we've also learned that having more than five direct reports can get pretty overwhelming, and so we've had to expand our org chart as we've grown for that reason.
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Great post. I think this isn't discussed enough (which just benefits those companies who have nailed it). This ties into one of my prime lessons of tech management: always be recruiting. To do that well, you have to start with an awesome idea/company and then hone your message to get the best people working with you.