A guide for applying to data science jobs

May 29, 2018

Emily Robinson gives advice on applying for a data science job (that you can likely generalize for most tech jobs). For example:

If you have a GitHub, pin the repos you want people to see and add READMEs that explain what the project is. I also strongly recommend creating a blog to write about data science, whether it’s projects you’ve worked on, an explanation of a machine learning method, or a summary of a conference you attended.

This is especially true for visualization-heavy jobs. It doesn’t have to be GitHub. You just need a place where others can see your collection of work, so that they can see if it aligns with what they’re looking for. Plus it lets you show off your best stuff.

And this:

Rather than applying to every type of data science job you find, think about where you want to specialize. A distinction I’ve found helpful when thinking of my own career and looking at jobs is the Type A vs. Type B data scientist. “A” stands for analysis: type A data scientists have strong statistics skill and the ability to work with messy data and communicate results. “B” stands for build: type B data scientists have very strong coding skills, maybe have a background in software engineering, and focus on putting machine learning models, such as recommendation systems, into production.

I’ve never formally interviewed for a data science job, and the last job I interviewed for was back in college I think. So I’m one of the worst people to ask about this stuff, but this seems like good advice.

Favorites

Best Data Visualization Projects of 2016

Here are my favorites for the year.

Where Bars Outnumber Grocery Stores

A closer look at the age old question of where there are more bars than grocery stores, and vice versa.

Real Chart Rules to Follow

There are rules—usually for specific chart types meant to be read in a specific way—that you shouldn’t break. When they are, everyone loses. This is that small handful.

Causes of Death

There are many ways to die. Cancer. Infection. Mental. External. This is how different groups of people died over the past 10 years, visualized by age.