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(continued from the previous post)

4) What are the best and worst parts of the job?

My favorite part of the job is probably something that I've mentioned above - the idea that I'm sharpening my data interpretation skills, that my job is basically to think soundly about data.

It's also great to see my ideas make a difference in the real world very quickly. People are having a different experience right now because of what I've done. My co-workers come to me for help with the data, and my recommendations determine how they do their job and how our customers interact with our product.

I also like the regular, but flexible, hours for my job. I don't have to worry about being "on-call" like some software engineers do, where they have to get up and do work at 3am on a weekend when the website totally breaks or something. I work during normal working hours, but I can easily switch around things like coming in late one morning for staying late another day.

There really aren't that many bad parts. I guess I sometimes end up waiting for the computer to process things, which you sometimes have to do when you're looking at billions of rows of data. Sometimes the work can get a little boring, when I have to repeat a similar, routine analysis. Some of my work ends up not having an impact, either because other people ignore it or because the conclusions end up not being actionable - and that's annoying for the same reason that making a difference is satisfying. But overall, the good definitely outweighs the bad.

5) What are the top 3 actions you took to successfully land a job in data science?

First, I learned a great deal of programming on my own. I picked up Python and JavaScript, because the school I was working at asked me to teach these, and because I wanted to do more with my blog. Some of these pre-career change projects can be found on the other posts on this blog. The programming skills, combined with the quantitative background I already had from my science background, positioned me well for a data science job.

Second, I applied to a data science boot camp. Insight is a well-known program, as is The Data Incubator. There are many others. Seriously, if you're wondering about a career in data science, apply to one of these programs now. Just the application process taught me a ton of things, ranging from "what does a data scientist do?" to "how do I scrape a webpage?" to "what specific tools do I need to become proficient in?" It will answer a lot of the questions that any potential future data scientist would have.

Third, I applied to jobs. Usual job seeker advice here - be tenacious, send to multiple employers, keep practicing interviewing, etc. If you've learned your craft well, you're tremendously valuable to potential employers - so believe in yourself and keep at it.

6) If you could give career advice to yourself from a couple of years ago, or to a fellow scientist or mathematician now, what would it be?

I'm happy with how things turned out - and I see now that I was already on this trajectory from 2 years ago - so I'd tell myself to keep going. As for anyone with a science or mathematics background contemplating a career change, I'd say go for it. At least, try applying to one of those boot camps.

I'd also say this, on a more "big picture" level: I think that computing, and data science in particular, is the pre-eminent field of our age, in our current moment in human history. I first got into physics because of people like Einstein or things like space travel or nuclear power - but these are very much 20th century endeavors. I still love physics, and there are certainly still some interesting activities there - but computing and data is where all the really exciting things are now happening.

7) Anything else I'm missing?

I can't think of anything really "missing" - so here's a summary instead. I love my new job, and my new career, considered as its own academic field. It has a number of varied characteristics that all fit my traits and needs exceedingly well: the required talents and skills, the reward schedule, the mix of group and solitary activities, the working hours, the connection with all other fields of study, etc. Of course, every job is bound to have some downsides, but I'm very content overall - I couldn't expect much more from a new career. If anyone is thinking about a career in data science, I'd encourage them to at least give it a serious pursuit.

(consolidated in the next post)

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Sherlock Bayes, logical detective: a murder mystery game
The want of a mate
Another post, from the table of contents