Data scientists are in great demand, and the skills shortage is expected to last for several years. Many businesses are looking for skilled employees that can fulfill the company’s technical requirements. What qualities distinguish an excellent data scientist? When looking for the ideal applicant, most companies and recruiters prioritize skills testing—hiring someone with no technical abilities, after all, may be an expensive error. Onn the other hand, successful data scientists have traits that a skill test alone cannot determine. They possess a variety of abilities and traits that cannot be learned from a book.
To get started, we’ve created a list of the five most significant qualities to look for in your next employee.
Data scientists convert data into information. Therefore statistical knowledge is at the top of our toolkit. Knowing your algorithms and how and when to use them is perhaps the most important aspect of a data scientist’s job. However, doing so successfully may be both an art and a science.
A technical interview focused on skills will reveal whether a candidate has a strong background in data science and big data analytics and whether they are excellent at statistical reasoning. However, it is the recruiter’s responsibility to verify this at the interview stage. While a candidate’s resume may state that they finished a data science course or not, it does not necessarily indicate how well they communicate.
A skilled data scientist can use a toolkit full of algorithms to model any data and generate statistically informed predictions and suggestions. A smart data scientist can detect anything ‘fishy’ in the findings he receives, recognizes that he has to ask the client or stakeholder a few more questions before withdrawing to the code cave, and can distinguish between a game-changing discovery and an expensive blind hunch.
During the interview, ask your candidates how they would resolve a question using statistics. This question will help highlight any candidate’s statistical thinking ability.
Although using data to solve issues is an integral part of the work, data scientists must also think outside the box in other areas. Because the business is still in its infancy, data scientists may find themselves without the necessary tools and resources to finish a task.
HR managers should seek individuals who can work around this challenge by completing tasks around the data science project with available resources. Alternatively, data scientists who understand what resources are required to complete the task and request these resources are excellent prospects. This will change when the industry catches up to the demand, but data scientists should work around the absence of technology and finish required data science and machine learning projects.
Data scientists create tools, pipelines, packages, modules, features, dashboards, websites, and more by writing code and collaborating with other developers. On both the backend and frontend, we develop code. When they can’t seem to find the answer they need, they investigate through unfamiliar formats and outdated code, and as a result, build our tools.
The spirit of a brilliant data scientist is that of a hacker. Because the gold standards in this sector change at an alarming rate, technical adaptability is just as vital as expertise. To ensure that we can move at the speed of demand, data scientists collaborate, support open-source, and share our knowledge and experience. If your data scientist is a quick student, you’ve made a wise investment that will pay you in the long run.
Most of the time, after the analysis is done, the outcomes aren’t pretty. That’s not to suggest they’re useless, but they’re frequently ensnared in obfuscated readouts or plots that appear intuitive to the expert but are cryptic to the rest of the team and stakeholders. An intelligent data scientist can use common ground, metaphor, skilled listening, and storytelling to contextualize and communicate an issue and its solution to people from all walks of life. Written communication for a statement of work or a report, visual communication for precise and intuitive plots and visualization, and spoken communication for presentations, data science project requirements, check-in meetings, and iterative design are all examples of this. When it’s apparent that no one is on the same page, your data scientist can call a meeting to a halt, create a diagram on the whiteboard, and extract consensus from a varied group; you’ve got an incredibly valuable member of your team.
Data science is progressing at a breakneck speed, and industrial advances will emerge from those working in the field’s ambition to enhance the use of data. Candidates that are curious about how data is utilized would be good team members since they can help the company identify new applications for the vast amounts of data it has collected.
A skilled data scientist will take a request, put it into action, and confidently produce the forecast or analysis. Because whatever he performed aroused that curious itch, a smart data scientist would ask for additional datao interview consumers, or attempt something new in the next version. Machine learning contests may irritate curious data scientists since they don’t have access to all the levers and options for asking questions and digging further. Curiosity masters are ready to challenge their preconceptions.
These five qualities of great data scientists can help you find the top individuals, whether you’re an employer or a recruiter. Make sure to search for individuals that have a strong mix of statistical thinking abilities, problem-solving skills, communication skills, technical understanding, and a healthy dose of curiosity when making your subsequent hiring. Data scientists that possess these characteristics will undoubtedly aid your company’s growth and success.