In 2012, The Harvard Business Review declared the role of a data scientist the “sexiest job of the 21st century.” Whether or not this still rings true today, the need for data scientists has grown and evolved over the past few years due to globalization, digitization, and trends in data personalization. And while data scientists are as in-demand as ever, these changes have made it more challenging for HR professionals and recruiters to evaluate whether a candidate has the data science skills needed for a role.
So, which are the most critical data science skills today? This article will explore the technical and soft skills all good data scientists should have and how to evaluate whether a candidate has what it takes to excel.
As one of the world's most popular programming languages, Python has become the lingua franca for those in the data science world.
According to a survey conducted by Kaggle, an online community for data scientists, Python is the most popular language among data scientists, and 66% of data scientists say they use Python daily. So, to collaborate effectively with colleagues, an understanding of Python is a must for data scientists at every level.
However, it can be difficult to determine whether a candidate has the Python skills necessary during the screening phase of the hiring process. For example, a candidate may list Python as a skill on their resume because they took a Python class or picked up a passing knowledge of the language through self-instruction but still lack the understanding needed to succeed in a data science role that requires extensive Python coding.
A technical recruiter can help, but evaluating programming skills through a phone screen is difficult, if not impossible.
But, by using an online skills assessment at the beginning of the hiring process, you can easily assess whether a candidate is sufficiently knowledgeable.
Whether you want to test a candidate’s ability to code entry-level Python algorithms, their Python debugging skills, how well they work with arrays in Python, or their understanding of Python objects and implementing data structures, there is a wide array of pre-employment tests available to test this critical data science skill.
2. Machine learning
Since data scientists deal with large amounts of information (it's not called Big Data for nothing), they rely on machine learning to analyze datasets. In addition to identifying relationships that may exist within a dataset, cleaning data by removing redundancies is a key machine learning skill.
The most skilled data scientists will be well-versed in techniques such as supervised and unsupervised learning, decision trees, logistic regression, and recommendation engines. And, fortunately for recruiters, data science tests are available to identify which candidates are most skilled in this area, so only the top applicants are shortlisted for the next stage of the hiring process.
Statistical abilities are a core element of the data science skillset. Statistics involves the gathering, organization, and interpretation of data and the ability to create mathematical and statistical models for data.
Highly skilled data scientists with the necessary level of statistical skill should be able to apply and expand on statistical models. They should also think critically about the value of various data and the answers it can and cannot provide.
4. Data analysis
It goes without saying that working with data is a crucial data science skill, but we'll say it anyway. All data scientists should understand how to handle data, perform basic data analysis, and interpret data.
This is another skill that’s difficult to test during interviews. But recruiters can use a data analysis test to evaluate whether a candidate grasps the fundamental concepts behind inputting, reading, and manipulating data.
5. Neural networks
A neural network is a machine learning algorithm modeled after the human brain. Neural networks assist in the analysis of unlabelled data by identifying correlations between variables in a dataset. A skilled data scientist should understand the concept and how to train and implement a neural network algorithm. An advanced data scientist should have a deeper understanding of how they work and how to optimize them.
6. Data visualization
Candidates for data science roles should be experienced in creating visual representations of data. Data visualization is a key competency in helping organizations make sense of huge amounts of data, and experience in using tools such as ggplot, d3.js, and Tableau is essential.
Soft skills are critical, even for a field as technical as data science. Among the soft skills necessary for data scientists, communication skills are paramount. As well as being able to work with, analyze, and interpret data, they must also be able to effectively communicate their conclusions to both technical and non-technical audiences so that their colleagues can collaborate with them and make decisions based on their findings.
Mathematics is a cornerstone of the data science field. Although it may seem obvious, any data scientist candidate should have a solid foundation of mathematical skills and knowledge. Calculus, linear algebra, and statistics are key mathematical areas in which candidates should be proficient.
How to assess data science skills
So, now you know the skills to look for when you're hiring a data scientist. But, how do you ensure that your candidates are qualified in the most important data science skills before you begin inviting candidates for interviews?
Since resumes and phone screen interviews aren't enough to measure a candidate’s skill level for such a technical position, pre-employment skills tests are necessary to quickly determine who has the required skills before investing further time in a candidate.
TestGorilla's data science test comprehensively tests candidates on a range of core data science skills, including statistics, machine learning, neural networks, and deep learning to help you identify the best candidates for your open position.
And, since these tests are administered to candidates before inviting them to interview, you will increase hiring efficiency and get the best candidate in the door quickly.
If you’re ready to test a range of your candidates’ data science skills, sign up for a free trial and see how TestGorilla helps technical recruiters make better hiring decisions faster.