What Do You Need to Consider When Hiring a Data Scientist?
Tags: Machine Learning, Startup, Interview, Data Science, Programming

Data Science Skills

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Statistics

One of the tasks data scientists perform is running experiments. This requires the need to hypothesis test and use p-values and that’s personally a baseline for statistical understanding.

Of course, there is a lot more to statistics than just understanding p-values. There are so many different levels of statistics that a team may require. For example, there is the concept of Frequentist vs. Bayesian statistics (which is usually a good intro question to gauge where someone is in their statistics journey). Some data scientists might only understand the high-level difference, while others might be keeping up with all the most recent updates on when to apply which method.

Perhaps you are looking for Ph.D.-level statistics to research and work on very abstract and ambiguous problems, or maybe you just need a data scientist that can perform basic analytics and run A/B testing.

These are two very different sides of the mathematical spectrum and their pay-grades will often reflect that.

Machine learning And statistical models

Along with general statistics, there are loads of models a data scientist can be familiar with. The generalized separation of model types starts with supervised vs. unsupervised, and then further continues to split into other model types, like classification and regression. Even here, there are further nuances like the concept of ensemble models.

Overall, it is very difficult for one data scientist to fully grasp every single model and or have implemented even half them into production.

Programming

Programming skills are interesting skills to look for, due to all the varying levels and nuances in programming. For example, some data scientists might only use Python and R for analytical purposes.

This means they might not be a good fit for implementing models into larger systems.

Other data scientists are not only great coders, but also DevOps-level engineers who are experts in Docker, Kubernetes, and CI/CD. They not only can create models but they can deploy your model to an enterprise system or AWS Lambda function.

This is really dependent upon what role your team plays. If your team focuses more on research and analysis, then you don’t need data scientists that can also program entire systems.

On the other hand, if your team is heavily entwined with a specific product and needs to deploy models, then having at least one strong programming data scientist is helpful.

SQL

One skill that anyone who uses data inevitably requires is SQL. But not every data scientist or researcher is proficient at writing super complex queries. Personally, we believe that pretty much every data scientist should know the basics of SQL at the very least; otherwise, you can’t even access the data you want, in many cases.

Now, if you already have a strong SQL expert on your team, you might be able to get away with not requiring every new hire to have SQL skills. But it is usually required.

Data visualization

Part of a data scientist’s role is communicating their findings with directors and managers to help drive decisions. One great way to do that is through data visualizations. This can be in Jupyter Notebooks, Tableau, and a list of other data visualizations tools.

Even in the data visualization category, there are varying levels. Some data scientists only are interested in learning just enough to create a report using a few basic charts, while others really enjoy creating beautiful dashboards that really allow managers and directors to drill into. So make sure you know which kind of data visualization skills you are looking for.

Product and business sense

One skill that is often overlooked is a person’s product or business sense. Not every data scientist on your team needs to be amazing at this. However, having one or two team members that are good at knowing where your team could find value in your business is key.

Just knowing how to run models and perform hypothesis testing is not enough as a data scientist. Companies will often have an infinite amount of problems to solve, and knowing where to find the best ROI helps justify a data scientist’s six-figure salary.

So you can’t just rely purely on a highly technical data scientist to find and create the right models. Having a team that has good product sense makes a huge difference. Otherwise, the team could be focusing all their brilliance in all the wrong places.

Deep learning for NLP And computer vision

Honestly, most deep learning work is done by software engineers, in our experience. Perhaps the data scientist did some research but the heavy lifting portion of the deep learning is done by the SWEs. Of course, that doesn’t mean companies aren’t looking for data scientists with an understanding of TensorFlow.

So we will add this skill set as well because we have seen it on job descriptions in the past.