Data science isn’t for everyone. Success requires an aptitude for math combined with creativity and curiosity. More succinctly, success is being able to manipulate the data into telling stories and giving insights.


If that sounds like a good time then read on.  You need to ask yourself some questions that will help you prepare for some of the less obvious requirements of the role.

  • Are you just in it for the money?  Salaries rise and fall and eventually stabilize around a market value.  Data science is hot right now… But it won’t always be.
  • Can you program?  Many of the tools for data science require the ability to script and/or do full programming.
  • Do you like to spend your free time learning new things?  In this rapidly growing field, you will have to work and learn to stay on top.
  • Do you have any tertiary skills? To make sense of financial data, for example, you have to understand finance.

Still interested? Great!  Time to get working. Here’s what you need to do to land a data science job.


  • Educate yourself.  There are lots of resources. In my opinion, if you already have some computer and math skills, you should take a condensed course, like the one at Galvanize. You could just educate yourself, but a certification carries a lot of weight with recruiters.
  • Network. Jobs are much easier to land when someone will pass your resume directly to the hiring manager.
  • Do a project. Find some data and make it tell a story. Preferably something that is interesting to the field you’d like to get into.  Start here for all the data you could ever want.
  • Familiarize yourself with the tools. You never know what tools you will need to use. Could be something like tableau or just plain old python.  The more tools you have the faster you will find success.
  • Find 5 target positions. Go to job search boards such as Glassdoor, LinkedIn Jobs, or Indeed and search for data science positions in your chosen industry. Try to search for a variety of terms instead of just “data scientist”. Try terms like data analysis, machine learning engineer, or quantitative analyst. Often times many people discover a problem with too many options rather than a few, so eliminate many of them.

At the end of the day, data scientist positions are competitive, but it’s all a numbers game. The best way to maintain momentum and positivity is to keep a pipeline full of opportunities you’re excited about.