Burtch Works recently hosted our first “Ask Me Anything” career planning webinar for data scientists and analytics professionals, where registrants could submit their top questions for our recruiting team to address. Flora Jiang, Karla Ahern, and Brian Shepherd all shared great insights, so we thought we’d post a few excerpts from their answers so that our blog readers could enjoy them as well.
We received a lot of positive feedback about the format, and the team answered a lot of other questions on career management, hiring trends, and more, so make sure you check out the full video here!
What are the keys to an effective resume for data science and analytics, especially when you are switching careers?
Flora Jiang: I would refer you of course to some of the resources that we have online including resume blogs, but to help answer their question, I would look at this from the lens of a hiring manager. There’s constantly a massive load of applications that are being submitted online. So it’s very important to be able to highlight what is important to make sure your strong points and your skillsets are coming through. I recommend no more than one or maximum two pages, since resume reviewers just don’t have time to look at every single detail on your resume oftentimes. So make sure the most important information is highlighted and really close to the top of your resume.
If you’re switching careers and you haven’t really worked professionally in analytics or maybe your professional work hasn’t reflected what you’re looking to move into, add in a section that I like to call “relevant side projects”. This may include academic work, bootcamp experience, other data-related projects that you’ve worked on etc. Make sure this is highlighted and speaks to the direction in which you’re looking to go with your career.
What is the best way to position yourself as an applicant who is relocating or applying for jobs in a new area?
Karla Ahern: So this is a question that we probably get on a weekly, if not daily basis, and I think the thing that we would recommend is really first and foremost to be honest on LinkedIn and on your resume about where you’re currently located. In both places, you can include a short blurb about where you’d like to relocate and when. For instance, “Currently located in Phoenix interested in relocating to the Denver area in Spring 2020” or something like that. If you’re moving for a spouse or a partner’s job you could even include that just so there’s a little bit more of a defined timeline around your move.
If perhaps you don’t have a specific location in mind and you’re just open and you want a change, you can put that on LinkedIn and your resume as well. And again, it can be under your name and your current location, something like “Open to relocating for New Opportunities”. I think it’s just equipping whoever is reviewing your resume with as much information as possible, and giving them a clear lens with which to view your resume, application, and candidacy for specific roles.
I’ve been applying to lots of data science jobs, but haven’t gotten any calls back yet. Do you have any recommendations?
Brian Shepherd: This is going to piggyback a little bit off of what Flora was talking about earlier. But I think it’s important to remember that hiring teams are really inundated with resumes, especially when looking for a data scientist, since it’s a very hot career. So one thing to keep in mind is a lot of the screeners on the hiring side might be scanning for key skills. Make sure to highlight those things really clearly in your resume when applying for roles.
It’s really important to review the job description and make sure your resume is really targeted to the specific role that you are applying to. And simple things like aesthetics do matter, so I always say it’s important to be careful not to overwhelm your resume with text blocks. Keep it straightforward when it comes to your resume: your background information right at the top, your education, your work history, etc. Include those things, and like Flora said keep it to one or two pages.
And just as a quick plug for Burtch Works – given that these hiring teams are so busy, one of the great perks of working with us is that we’ve got a direct line to the hiring team. So we can kind of cut through that black box of online applications and make sure your resumes are actually getting into the right hands of the right people.
How do you view data science bootcamp graduates in the recruitment process?
Flora Jiang: As a recruiter here that works with a lot of entry-level or more early career candidates, bootcamps are a topic that I hear about pretty frequently. There is no simple answer because it really does depend on the program, because I would say that bootcamps are not all created equal. Some are great, and some, I think, are still in emerging stages.
I don’t necessarily think that bootcamps will quite replace the value of a Master’s degree in say statistics for analytics or data science professionals. However, that being said, I think boot camps are a great way to give professionals that extra edge, especially when they already come from pretty quantitative background, maybe just moving from more of your traditional analyst role and looking to acquire that extra statistical modeling experience. Sometimes this is that extra little edge they need or something to add to your resume and to incorporate those projects as I mentioned before. They can be a great way to pivot into more of that data science or advanced analytics role.
It feels like the statistical software options are expanding every day. How do we stay current and what programming language are employers looking for? Where can I learn it if I already have the degree in hand?
Brian Shepherd: An advantage to our position as recruiters is that we really stay in touch with the community to get a feel for what programs are in demand. We do our SAS, R, or Python survey every year and we really appreciate everyone’s participation in that. What we’re seeing is Python is definitely the program of choice for most of our clients and when we see folks looking for data science opportunities, Python is going to be extremely important.
There are organizations, maybe some in the Financial space, where you’re seeing more SAS, but if you’re looking to get into some of these traditional data science roles Python is definitely the way to go when it comes to learning and really honing your skills. I think because data science is such a hot topic, a lot of organizations are really putting a lot of focus on their advanced analytics teams. A lot of organizations even internally to their current employees will offer brown bags or lunch and learns. So when that’s offered with your current employer, it’s always a good opportunity. It’s free and a good opportunity to hone those skills.
There’s online resources, such as DataCamp, Coursera, Udacity, etc. These are all online programs that can really focus on specific programs where you’re looking to develop your skills. If you have the money and the time, bootcamps are always a great way to go. I know we’ve worked quite a bit with Metis in the past and we’ve done some blogs with them about the growth of Python in the data science space. So those are some of the options that you could look into.
Transcript has been lightly edited for length and clarity.
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Check out the entire Data Science & Analytics Career Planning video below, where our recruiters answer even more job search questions on resumes, tips for students/new grads, tools to learn, career management, evaluating opportunities, and more! Check out their insights in the video below.