So you've decided you want to hire a data scientist. What kind of considerations should you make when writing the job description? How can you find the best person for the type of work you want done?
First we look at what the definition of what a data scientist is. Data scientists are generally expected to be great at coding, math and statistics, graphic design, business understanding, and translating technical knowledge into business insights. The coding allows the data scientist to combine data sources and transform the data in a usable form. They may also use their coding skills to create software, apps, or APIs to function as a front-end for a machine learning algorithm or predictive model. The mathematical and statistical skills are at the core of a data scientist’s worth to your organization. These are used to build the predictive models, machine learning algorithms, tests for significance and appropriate data structures, and optimization calculations. From there, a data scientist must be able to transfer these algorithms in real value for the organization. This means they must understand how your business works, translate complicated technical projects into insights and uses understandable by business line owners, and create visualizations that convey these ideas. How can you find someone like this, you ask. Easy, a typical data scientist is pictured below.
Since there is no one, or at least very few people, that can say they are experts in all five of the areas core to data science, you need to outline which features are most important to your company’s needs. Determine what kind of projects your new hire will be working on. If you are looking to build a piece of software that utilizes machine learning, it may be most important that the prospect have high coding and analytical ability, while they may not need excellent graphic design or presentation skills. If your data scientist will be primarily working on predictive models that will be used in the business, some coding ability can be sacrificed for better business and graphical design skills. Some of the tasks may be able to be done by an existing employee or by someone cheaper than a data scientist, such as an intern.
If you have a data scientist who has great analytical, graphic design, and presentation skills, but has less experience doing data extraction, loading, and transformation (ETL), you can generally find someone else to do this function. One thing you should not worry too much about is specific software or programming languages, as someone who knows how to program can easily learn a new language or data structure and someone who knows many different software programs can pick up a new one with little trouble.
Now that you have decided what skills are most important to your open position, you need to attract the right people to apply. If you want someone who can hit the ground running, make sure you set the pay correctly at 6 figures. To take a cheaper route, you can hire someone junior with little experience, however, you will need to give them more time to learn on the job. Of course, once these data scientists have up-skilled sufficiently, they will likely expect more pay and go looking for it if you don’t offer it. It can help your case if you offer some other benefits, such as more annual leave, flexible working hours and/or location, or subsidized education. You could also try shorter working hours with the same amount of work, encouraging efficiency and high productivity for the hours they are at work.
There are plenty of blog posts and articles that list data science interview questions. I’ve listed some below. Most data scientists wouldn't be able to answer every one of these, nor would all apply to the position they are applying for. Though these might be a good starting point, make sure you understand what you are asking and adapt the question to relate to your business, or you will not know if the interviewee answered appropriately. If you aren’t technically inclined and don’t have anyone available who is to sit in the interview with you, it is best to ensure the data scientist is able to translate technical information into business language, as this will likely be important for their position. I caution you from including an exercise or presentation requirement in your initial interviews. There are plenty of companies looking for data scientists and any barriers to the first interview should be avoided. A short exercise or presentation can be included in second interviews, or you can ask for a past project – anonymized – after the first interview.
In short, finding the right data scientist for your organization can be difficult, but if you keep your expectations realistic, it’s not impossible. Data scientists by nature are curious and love to learn, so once you have them, you can often keep them in your organization by keeping them interested and engaged.