[mdisc]

1. I think it’s better to focus on doing - especially if you’re interested in working with an earier stage company. You’re much more versatile and if you choose the right company with an upward trajectory then you have the chance to specialize more into data science and learn model building if you want to. Also, data science seems sexy, but I find it most rewarding when you can put your own models into prod and also I think it’s useful for people to have the context around what it involves before they specialize. I’m 28, and had a lot of peers go into data science and quickly realize that it was the hype that lead them there and that they enjoy engineering more.

2. Look for work in a different part of the country... or maybe just the right organization that’s willing to take a chance on you. We’re in Austin and we’ve hired smart, hardworking kids who’ve never touched the languages we use and get them contributing meaningfully in <2 months.

3) I used to work for a startup where our CTO would not hire a data scientist unless they could write production code (in backbone and rails which I didn’t know at the time) and after I started, I spend 4-5 months just learning to be a full stack dev- I think that was so useful for my career as a data scientist. It meant that data scientists at this company could put whatever models they were running into the product- it drastically simplified org structure—- much more autonomy and fewer project management dependencies.

Sure not a lot of data scientists wanted to also be or make them selves into full stack developed, but you’d end up with the really gritty ones who and they’d end up being more loyal and much more on the same page as the rest of the engineering team it was way better for the whole org.

We’re hiring interns + junior full time people by the way

https://angel.co/schoolinks/jobs