This post is contributed by Sandy Marmitt, Burtch Works’ analytics recruiting specialist.
Chances are, if you’re reading this article then you don’t need us to tell you that data science and analytics are hot career areas right now! Over the past few years, there has been an explosion of opportunities, as well as a flood of interest from students and mid-career changers alike.
However, since the market has changed so drastically over the past few years, it’s crucial for data scientists and analytics professionals to be smart about how they approach job searching and career management. Yes, there are more opportunities, but, as more professionals enter the market, many companies are becoming pickier about who they hire, and are adjusting their hiring processes to prioritize different criteria than they were even just a few years ago.
Here is our list of tips for data scientists and analytics professionals looking to navigate today’s hot hiring market:
1. Embrace the competitiveness!
There is no harm in talking to different companies and exploring your options. In fact, looking through job descriptions and evaluating opportunities can be a great way to learn more about what you want to do. It can also help you make sure that you’re learning the data science tools or analytical methods that are most in-demand in the roles that you want. Although employers may look askance at someone who job hops every few months, our research has found that many analytics professionals are changing jobs more often, and keeping one ear to the ground can be advantageous if the perfect opportunity comes along.
2. Be deliberate when making your career choices…
Before evaluating job offers, take the time to check in with yourself about which direction you want your career to go in. Do you want to expand your machine learning skillset? Or are you targeting a specific industry? What kind of data do you want to work with? Make sure that the roles you’re applying to are in line with the direction you want your career to take, as well as a good cultural fit.
3. …but make sure you’re being decisive
Since the hiring market is so hot, it’s important to be decisive. Many data science and analytics teams are growing fast and looking to hire quickly, so if you hesitate too long to commit to an offer you may lose out on the opportunity. Checking in with what you want out of future roles before you start searching can make your evaluation process that much quicker once you’ve reached the offer stage.
4. Look beyond just compensation when evaluating job offers…
On a similar note, it can sometimes be tempting to fall into the trap of accepting an offer based solely on a higher salary. Although compensation is obviously an important piece, make sure the opportunity you’re seeking offers room for learning and advancement, and keep in mind your long-term goals. Check out this post for more about how data scientists and analytics professionals can manage their career growth.
Job titles in data science and analytics can vary significantly from company to company. It can be hard to discern from job title alone whether the responsibilities, seniority, and the position itself are in line with where you want to be. Asking questions (such as the ones in this post) about who the role reports to, responsibilities, tools and methodologies used, types of data, and how much time is spent on different aspects of the role (analysis vs. data management) can all give great insight into whether the role will be a good fit. Since titles can vary so much, accepting (or rejecting) an offer based solely on the title might not be your best move.
6. As data science and analytics roles become more specialized, don’t get discouraged
One of the key trends we highlighted in our latest data science salary study is the increasing specialization of data science and analytics roles. As established data teams are growing larger, we’ve seen roles become more specialized, with the focus being less on “unicorns” who can complete every task, and more on hiring multiple specialists with different backgrounds who can collaborate as a team. This might mean that you’re not be a fit for every role, but don’t let that discourage you! With more opportunities than even before, you’re bound to find something that’s a good fit.
7. Know that flexibility on skill requirements can vary significantly
With this trends towards specialization, you may notice that some companies have an extensive list of skill requirements they’re looking to find, and seem unable to compromise if you’re missing even one of them. Others may be more willing to mentor, and look for potential to develop an employee. This can sometimes depend on whether or not the role you’re applying to is replacing someone who has left (a backfill), and the position needs someone who is all ready to hit the ground running. For a role that is completely new, a company might have more leeway to teach skills on the job. If a company can’t compromise, this might not necessarily be a reflection on you.
8. Be prepared to give presentations in your interviews
With analytics and data science moving “from the back room to the board room”, it’s becoming much more common to see employers testing potential hires on their ability to present technical findings and quantitative insights. Presentations have become much more common in interviews, even for highly technical positions, so earlier this year we put together a list of tips on how you can prepare and make sure your presentation is striking a good balance between technical details and business insights.
9. Be prepared for tests and assessments on the skills you’ve listed on your resume
Another outcome of the increased interest in the data science and analytics fields is that companies are looking for better ways to evaluate technical skills. Anyone can put “Data Scientist” on their resume, but employers want to know if your R or Python skills are actually robust, or if your only experience was a single college class a few years ago. Be realistic about the tools on your resume, and know that you may be given real-time tests or practical assessments so that you can demonstrate your proficiency.
10. If you’re turned down for a role, ask for feedback
With the market becoming more competitive, and roles becoming more specialized, it’s unlikely that you’ll be a perfect fit for every role. If you’re turned down, see if it’s possible to get feedback on how you can improve. Sometimes the feedback may have more to do with a company’s specific vision for the role, or other times it may be specific skill areas where you can improve. It’s a great opportunity to become more aware of your own skillset and to use the feedback for career advancement!
So far, data enthusiasm shows no signs of slowing down, so it’s very likely that there will be even more shifts in the hiring market over the next few years. We’ve been keeping our eye on how the market is changing, but stay tuned to the blog for more insights over the coming months!
Best of luck in your job search, and be sure to connect with me on LinkedIn!
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Want to learn more about data science and analytics career planning? Watch the webinar recording below to hear our recruiters’ best advice on interview preparation, tools and skills to learn, salaries, and more!