Three Keys To Hiring A Great Data Scientist
For a start, don’t let yourself get discouraged: The perfect job candidates are out there. All it takes to find them are a few tweaks in your search and selection process. If you’re ready to take full advantage of the opportunities big data has to offer, just read on for our three keys to picking great data scientists.
1) Have A Clear Understanding Of Your Needs
Before you start spending money or even your valuable time on the search for a data scientist, make sure you understand exactly what you’re seeking to analyze. There are a lot of different professionals working under the umbrella of the term “data scientist.” Do you need help with analyzing data about machines, or data about human behavior?
If your company needs to convert marketing data into actionable intelligence – targeting ads better and building recommendation engines – you’ll be looking for a candidate with a strong grounding in computer science and mathematics. On the other hand, if your goal is to build a narrative out of customer feedback and performance data, candidates trained in the social sciences – sociology, economics, and allied fields – will be most helpful. Data scientists with sociological backgrounds are great at chiseling useful conclusions out off ambiguous data.
By zeroing in on your true needs at the start of your hiring search, you can concentrate your attention on a narrower slate of highly-qualified candidates.
2) Concentrate On Finding The Right Talents; Tech Is Teachable
Your dream field of candidates would all come to your door with extensive programming and computer science skills. They’d be equally adept with analytics tools like Sequel, Python, and R. If you’re willing to become more flexible with regard to easily-trainable tech skills, you can give yourselves a significant leg up on competitors when hiring data scientists.
At this level, the ability to manipulate specific software tools is not nearly as important as the ability to think analytically and manipulate data in imaginative, useful ways. These talents can’t be improved with in-house training. Most good data scientists share a notable ability to translate complex data into simple, actionable conclusions. A candidate who lacks this ability will never be able to outperform one who has it, no matter how familiar the first candidate is with your favorite software tools.
Don’t overlook the mentoring and team-building opportunities you get if you commit to training your data scientists. Good training doesn’t just polish up your new scientists’ technical skills; it also teaches them to be an integrated part of your working team. This post from Harnham may help you.
3) Be Fearless About Looking Outside The Box
Because data scientists are in especially high demand right now, this is the perfect time to consider candidates from unconventional backgrounds. Today leading technology firms secure the services of the best and brightest young candidates straight out of universities. A willingness to look further afield can connect you with many highly-qualified candidates who may currently be working outside the data science field.
Besides enlarging the possibly-anemic pool of talent open to your company, looking outside the box is also a great way to build a more diverse analytical team. Incorporating multiple viewpoints – with professionals of different ages, races, genders, and backgrounds – will lead to a more effective and well-rounded team. The ability to incorporate new perspectives leads directly to more creative problem-solving and an increase in innovation that will serve your organization well in the long term.
You should also be on the lookout for non-traditional educational programs to feed your need for data science professionals. The burgeoning demand for experts in this field has led to many new training opportunities, and selecting candidates from these alternative sources can help you build a dynamic, competitive analytical team.
Because more and more companies are looking for data scientists every day, it can be hard to find recruits with impeccable educational credentials and perfect technical skills. The good news is that neither of these qualities is a true must-have. Any data scientist with the right talents and attitude can become a productive member of your team. Focus on those core values and the peripheral qualities, like technical proficiency, will take care of themselves.