This post is contributed by Burtch Works’ data science recruiting team.
With the data science field continuing to feel the effects of Big Data hype-tsunami, data scientists and machine learning engineers are some of the fastest-growing positions on LinkedIn. As a result of this growth, we’ve noticed some changes in what employers are looking for when evaluating data scientists.
A few years ago, we wrote a very popular post about the “must have” skills you need to be a data scientist, which still holds as a recommendation for necessary, baseline skills. If you’ve already got a solid background in the skills covered there, let’s take a look at some of the skills and hiring trends that have cropped up more recently!
1. Specialists are overtaking generalists
Contrary to a few years ago, when most data scientists were expected to perform tasks at every stage of the analytics lifecycle, now we’re seeing clients really drill down in specializations. As data science teams continue to grow, it’s more likely that data scientists will be working with other data scientists, analytics professionals, and data engineers, so the team may look to balance its specialties.
As we’ve pointed out, a company that is hiring its first data scientist or a small team may still look for “unicorn” generalists who can wear every hat, but data scientist backgrounds and skillsets have been diversifying as teams grow.
Recently, we’ve worked on a slew of opportunities that require more specialized expertise. For example, a healthcare company in LA is looking to add a natural language processing (NLP) expert to their growing team. Or, a tech company in Seattle is seeking someone with expertise in deep learning, specifically using TensorFlow and image processing.
The trend towards specializations is also happening as companies are able to really focus on the specific use cases for data science that are relevant to their business. At companies where there are many, varied use cases and a smaller team to manage them, generalists may still be in hot demand.
2. Use of data science is spreading to more industries
As we’ve found in our data science salary studies, the tech and financial services industries continue to be the largest employers of data scientists (54% combined), we’re seeing more opportunities cropping up across industries including Industrial, Retail, Insurance, Healthcare, and Agriculture. Most notably, these opportunities are not simply limited to Silicon Valley startups, but taking hold in more established corporations across the US. This is leading to increased geographic variability in the data science market with some companies even building out remote-based data science teams.
We’ve seen industrial firms hiring data scientists to analyze and manage growing Internet of Things (IoT) systems, and healthcare firms looking for data scientists who can wrangle claims and patient data, including seeking out experts in NLP. No doubt this industry expansion will lead to increasing opportunities for data scientists, especially for those outside the West Coast.
3. Senior-level Data Scientists MUST stay hands-on
One of our top predictions for the data science and analytics fields this year was that staying hands-on would be more important than ever, especially for senior-level data scientists in management positions. I regularly recommend to senior-level data scientists to stay as hands-on as time allows. In a field where tools and data usage evolve so quickly, if you spend too much time away from the data you may quickly find your skills and knowledge out of date.
If you’re unable to get this hands-on time at work, I would call attention to experience with Kaggle competitions or other non-work projects that can speak to your skills. Many companies are looking for leaders who can be what we call a “player coach”, in that they can be hands-on enough to mentor the team as well as lead. This is especially true as data science expands rapidly into new industries and leaders will often be the first hire to a team.
4. Expect the interview process to take multiple rounds
As companies have refined their approach to sourcing and evaluating data scientists, the interview process has become more extensive than in years past. Previously, companies would move qualified data scientists quickly through the interview process from phone screen to onsite interview to offer, especially since demand was great enough that these candidates might be fielding multiple offers at a given time.
Now, it is not uncommon for there to be 2-3 rounds of phone screens followed by an onsite followed by another round or two of phone screens. Companies are looking to ensure they’re building teams that have both the technical chops and business acumen. Data scientists should expect the process to take longer, and be diligent about giving all aspects of the process their best effort.
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