If you are a career switcher, that’d mean that you already have a substantial amount of professional experience. I’m sure you wouldn’t want to start in a junior position when changing your career into data science. And that is not an unreasonable expectation. All those years you were working should count for something right?
That’s why you need to highlight what sets you apart from the newly grads who want to get into junior positions.
Here are some quick tips to keep in mind while applying for data science positions. These will help you convey your value and make sure you will not be considered a beginner data scientist but a seasoned professional that will significantly contribute to the organization.
Specifically, if you have managed people or projects. Highlight some challenges you faced during these projects and mention them during your interview or cover letter.
Your professional experience, no matter how irrelevant to data science, will be the thing to set you apart from the newly grads and will get you an above-junior position. Do not hesitate to talk about your previous positions. Talk about the problems you had, how you overcame them, what this experience taught you and how you apply this new understanding at work right now.
During your interview, do not talk about how you can’t wait to use ML algorithms. Over excitement about machine learning tends to make one seem amateurish. This is even a very common gag inside the community.
Showing that you know this is crucial. That is the trait of an actual, established data scientist. Make sure you put enough emphasis on domain knowledge during your correspondence with the company.
Never say you don't have any questions to the interviewer. Ask relevant questions during the interview on topics such as:
This will signal that you know the general pipeline and are on a higher level than a newly grad with no professional experience.
This might not be possible for everyone, but if it is, try to do projects with data in your current role. Could be as small as visualizing some data or as complicated as training a model. That’d be great if you could have your work used by your current company. It will help you understand the real-life professional data science struggles better. But more importantly, it will show your future employer that you have worked with data in a professional environment and you are eager to improve yourself.
The main message we’re going for when applying to a data science position is: "I know I have not worked as a data scientist before but I have valuable professional experience, I am competent and I already taught myself a lot about data science. You should hire me because I will bring in a lot of value."
If you read my article "The unspoken difference between junior and senior data scientists" you already know that one of the key differences between junior and senior data scientists are management skills. After all, companies look for people who can manage real-life, challenging situations. Sometimes even more than technical excellence. So make sure to have your professional experience shine!
Let me know if you have anything to add to this list. I'd love to know what your tactics have been for applying to higher-level data science positions.