In recent years, data science has completely transformed the commercial landscape. More businesses than ever before are using data scientists and making decisions based on their findings.
The need for professionals who can analyze and draw conclusions from the massive volumes of data produced is rising. Even so, it remains challenging for many firms to recruit and keep outstanding data science candidates.
Understandably, leading tech companies are always on the lookout for exceptional data scientists, machine learning engineers, and data analysts. Amazon, Apple, Google, Intel, and Accenture are among the top recruiters who regularly hire data scientists in bulk. The below graph shows the share of data scientists working in the top tech companies globally.
Source: Towards AI
One of the major challenges HR managers face while recruiting data scientists is assessing their skills.
Many recruiters wonder what are the qualities of a good data scientist and what are their essential skills. This is because of the technical nature of the domain and most hiring managers lack first-hand experience with it.
What are the Qualities of a Good Data Science Candidate?
Many believe the world is in a digital age but a report by Salesforce pointed out notable deficiencies. The report suggests that the current workforce is plagued by a massive skills gap, mainly in their technical abilities. The Salesforce report says less than 20% of Gen Z respondents don’t believe they have advanced skills like data encryption & cybersecurity, coding, and Artificial Intelligence.
On the other hand, the same report also says that 82% of respondents plan to enhance their technical skills. Similarly, data scientists should also have a diverse portfolio of strong technical skills and a few soft skills and abilities to make them excellent at their jobs. Here are the essential skills and qualities of a good data scientist:
Technical Skills
Due to the technical nature of the field, a proficient data scientist must have an arsenal of advanced skills. Data science is a multidisciplinary domain, a combination of computer science, advanced mathematics & statistics, and coding. Therefore, a person aiming to build a career in data science must have a solid understanding of the above-mentioned domains.
In addition, a competent data scientist should have the below-mentioned technical skills:
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Statistical Analysis
The practice of gathering and analyzing data with the purpose of identifying patterns and trends is known as statistical analysis. It is a technique for eliminating bias from data evaluation by using numerical analysis. This method is beneficial for gathering research interpretations, creating statistical models, and organizing surveys and studies.
Statistical analysis is one of the primary skills needed for exceptional data scientists. Hiring managers should scrutinize the statistical analysis skills of the data scientists they want to recruit. They should go through the candidate’s background and look for projects and assignments that show a statistical mindset.
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Machine Learning and Deep Learning
A subfield of artificial intelligence (AI) and computer science called machine learning focuses on using data and algorithms to simulate how humans learn, gradually increasing the accuracy of the system. This incredible technology creates computer programs that can automatically access data and carry out tasks via predictions and detections, enabling computer systems to learn from experience.
Meanwhile, deep learning comes from a larger family of machine learning techniques built on artificial neural networks and representation learning. It is a field that relies on studying computer algorithms to learn and advance on its own. Deep learning uses artificial neural networks, which are created to mimic how humans think and learn, whereas machine learning uses simpler principles.
A talented data scientist must have deep knowledge and understanding of these two technologies to excel in a competitive environment. These technologies lead the way in how future machines will interact with each other and humans, and data scientists are the people who make it possible.
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Data Visualization
The graphic depiction of data and information is the focus of the interdisciplinary topic of data visualization. When interacting with large amounts of data or information, such as a time series, it is especially effective. These informational visual representations make complex data relationships and data-driven insights simple to comprehend.
Data visualization is done through sophisticated tools like Tableau, Infogram, Datawrapper, and JupyteR. A successful data scientist should be able to easily create and understand complex data visualizations and tools mentioned above.
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Programming Languages
As mentioned earlier, coding is an essential component of data science, and today’s data scientists are expected to be adept at multiple languages. Python, R, SAS, C/C++, Javascript, and PHP are some of the well-known languages a good data scientist should know.
Experts suggest a data scientist’s knowledge and expertise in a particular programming language like Python is essential. Python has cemented itself as one of the advanced languages preferred by highly talented data scientists globally. Year after year, data scientists have voted Python as their favorite programming language. The below figure shows Python as the leading technological asset of modern data scientists, beating RapidMiner, Tableau, and many others. 
Source: KD Nuggets
Therefore, hiring managers should always prioritize candidates with exceptional knowledge and experience in Python. They should look for applicants with a demonstrated history of working with Python and similar programming languages like R, SAS, and Javascript.
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Mathematics
It goes without saying that an expert data scientist should have immaculate mathematical skills. Data science is a number-intensive discipline with a heavy focus on a few critical areas like statistics, probability, and data analysis. Besides these, data scientists should have a robust understanding of complex mathematical topics like Matrices, Linear Algebra, Vectors, Calculus, Probability Distributions, Fourier Series, Regression, and Geometry.
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Big Data
Big Data describes data sets that are too big or intricate for conventional data-processing application software to handle. While data with more fields have more statistical power, they may also have a higher false discovery rate than data with fewer fields. Big Data has numerous applications in data science and understandably, data scientists are expected to be well versed in it.
Furthermore, data scientists should also have a firm grasp of well-known Big Data tools used by multinational companies and research organizations. These include technologies such as Hadoop, Knime, Talend, RapidMiner, Lumify, and MongoDB.
Other Essential Qualities Of a Good Data Science Candidate
Besides the technical intensive skills mentioned above, data scientists should have other qualities that may help them excel in their careers. These could be called Soft Skills or abilities related to their personality traits. Here are some essential qualities of a good data scientist:
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Detail Oriented
Attention to detail is one of the primary abilities needed to be a successful data scientist. Data scientists have to analyze massive amounts of data and go through hundreds of lines of complicated code. A small error in a database or a code could hamper the entire process and lead to unexpected details. Hence, a data scientist should be detail oriented and have a keen eye for minute intricacies.
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Interpersonal Skills
Interpersonal skills are the abilities of an individual to interact with other human beings. These skills are considered necessary for people-oriented jobs like sales or Human Resources but are equally important for technical roles like data scientists and bioinformatics professionals. Data scientists work in teams of several people and must coordinate with them effectively, making the need of interpersonal skills necessary.
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Critical Thinking
Analyzing the information at hand—facts, evidence, observations, and arguments—in order to make a decision is known as critical thinking. The topic is complicated, and there are many various definitions of what it means to analyze or evaluate factual data in a reasonable, skeptical, and objective manner.
Data scientists are expected to be critical thinkers and analyze everyday business problems purely on facts and information. A critical thinker will have a problem-solving attitude and will be a valuable asset to a data science team.
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Creativity
Utilizing data to see how things may be handled differently to produce more value is at the heart of data science. Consequently, as creativity enables the development of something from nothing, it is an essential quality of a great data scientist. A creative data scientist will find innovative solutions to complicated business problems and present data in a more meaningful way.
If you’re interested in learning more about how to hire the best data scientists, get in touch with BenchPoint right away. We’re one of the top healthcare IT recruiters in the US region. We’ll show you how to maintain an evergreen candidate pipeline.
We’re dedicated to ensuring that our clients acquire the most talented candidates while saving costs in the process of doing so.