AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
![]() Make sure your email address is appropriate. We’ve seen exactly four data science resumes where the email address on the resume was incorrect. Storytime! When our co-founder was first applying to jobs out of college, he realized about 20 applications in, he had spelled his name “Stepen” instead of “Stephen.” Don’t pull a Stepen.ĭata suggests that when your email is wrong, your response rate from companies drops to zero percent. The takeaway from this section is simple: this is not where you should make a mistake. There are tons of fish in the job market sea you just need a fishing rod. And our scraper has a lot of room for improvement, so that’s significantly lower than the actual number. Our scraper that indexes jobs across thousands of company websites shows over 5,000+ full-time data science job openings in the US across all tenures and skill sets. We can assure you there are all kinds of data science jobs available. If you only have a handful of tools under your toolbelt, but you can use them effectively to answer questions with data, you’ll be able to find jobs looking for that skill set. So that means if you’ve read a few articles on Spark or adversarial learning, but you can’t use them in code, they should not be on your resume. The rule of thumb that we recommend you use in determining whether to include a skill on your resume is this: i f it’s on your resume, you should be comfortable coding with/in it during an interview. If you’re changing your resume in small ways for each job you apply to (for example, put Python for jobs that mention Python and R for jobs that list R if you know both), you’ll have no problem with those keyword filters. The reason people make such an exhaustive skills section is to get through the mythical data science resume keyword filters. This is a big red flag for hiring managers. A quick rule of thumb: if the skills section takes up a third of the page, it takes too much space. It’s a laundry list of skills in which no one person could have expertise. ![]() The most common mistake we see on data science resumes (that we used to make on our resumes) is what we call skill vomit. Which of these two ways to describe reporting is more compelling? Numbers draw attention, are convincing, and make your resume more readable. Example: Since you built a customer segmentation model to determine how to communicate with different customer types, customer satisfaction is up 17%.Example: As a side project, you built a movie recommendation engine that now saves you 26 minutes each time you need to decide which movie to watch.Example: You ran an experiment across different product features, which resulted in a 25% increase in engagement rate.Example: You built a marketing attribution model that helped the company focus on marketing channels that were working, resulting in 2,100 more users.Example: You built a model to predict who would cancel their subscription and introduced an intervention to improve monthly retention from 90% to 93%.Example: You developed a pricing algorithm that resulted in a $200k lift in annual revenue.Incorporate those specific projects into your resume. See if any projects you’ve worked on come to mind while reading it. ![]()
0 Comments
Read More
Leave a Reply. |