The data scientist is known to be the topmost career of the 21st century. With the wealth of raw data that is increasing day by day, companies are overly looking for professionals who are able to encrypt numbers and extract valuable information from them. Of course, the benefits of this work cannot be compared. You get a wonderful profile, decent salary and chance to develop your career in the most progressive field. However, it is important to note that not everyone is able to respond to these requests. In the United States alone, there is a 50% gap between the supply and demand of data science professionals, and demand will continue to grow. This is because, in most cases, applicants are unable to meet the criteria for this type of job. Regardless of whether it is a lack of skills or expertise, companies are unwilling to compromise on this. With that in mind, we’ve created this quick and practical guide to 7 factors companies look for when hiring a data scientist
As a data scientist, you have to work with a large amount of data that cannot be processed with excel. For that, you have to know some languages like Python and R. these are some languages that laid the foundation of data science.
Most of your day-to-day work may involve working with Python and its libraries, where you manipulate large amounts of data, refine unstructured data, process and visualize data, and put it into a presentable format for analysis. If you’re on the analytical side, you’re more likely to use R for statistical models like data regression, cluster analysis, decision trees, etc.
In any case, the mastery of one or the other, especially Python, due to its versatility, makes a significant contribution to you asserting yourself as a data scientist. After all, companies need people who can work effectively. Include some pet portfolios/projects on your resume that demonstrate your familiarity with languages.
If you do not like math, it is better to focus on another area of work. Data science has a strong mathematical focus, with the basics of statistics and the probability being used daily to generate practical information. By default, data scientists need to process large amounts of data and use their mathematical knowledge to create statistical models that can shape key business strategies and provide valuable information about their performance.
Complex equations are often dealt with and broken down into simple, easy-to-understand conclusions. Ideally, you should be able to handle linear algebra, calculation, and of course statistics and probabilities. Remember that what matters here is not the theoretical knowledge, but the ability to put it into practice. You also need to know which concept to use and when.
This is said but it is true enough that companies look for those candidates who are having good analytical thinking and problem-solving skills. There may be a number of different ways to get the information from the raw data but it is up to you how you identify the most efficient information to process it. And which strategy will give you the most accurate information? This all depends on your analytical thinking or problem-solving skills. All overall we can say that you have to use your brain to analyze the data and to process the data in an efficient manner. No one will come to you to teach all these things only you have to do this.
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As a data science expert, you work closely with different departments in your company to better understand the data and the problems associated with it. This means that there can be no gaps in the communication of ideas and problems with others.
It is the same with people who pay you a lot of money for processing the numbers, no matter how you came to your conclusions. You only care what you can do with it. You have to present your results to the team and how you present them is paramount. If you become too technical with the numbers, you lose their interest. If you can give them practical ideas and solutions, that will give you more importance in front of them. It is not the data that you have that is important; it is the way you ultimately present it that makes or breaks your case.
Good communication skills are therefore essential to be successful in the data science industry. You will interact with customers and your team members daily – you have to do it right; otherwise, it can be very costly for the company
Machine learning and artificial intelligence are not only buzzwords they are the two important concepts towards which data science is moving. Companies are moving to these technologies to handle their data sets and computing power. So if you have knowledge of machine learning and artificial intelligence then your career is going to be top level. Having knowledge of these technologies will open the door to a new career for you.
Early you get the knowledge of these technologies; the better you can put into your data science techniques.
Companies like those applicants who think from a business point of view. In the end, you are there to help them, and if you are already one step ahead, this is great for you. This only makes work easier and eliminates layoffs that can arise from misunderstandings.
Since it is your job to process the data and present the information to the company, it would be beneficial if you could consider this approach from a business perspective. Take the place of management and think about their expectations, problems, and perspectives.
What good is it to become a data scientist if you don’t understand the numbers you are dealing with? At the end of the day, you should know why. You may be calculating the numbers for a hedge fund every day, but if you don’t understand how this affects the performance of the company, the markets, or your customers, everything becomes a contentious issue.
For this reason, many companies value their potential data scientists with in-depth expertise. This includes understanding general industry trends, past performance, future prospects, market trends, and the company’s position in the industry, its competition, and other key aspects.
It’s not just about numbers and coding, it’s about understanding them, and the only way to do that is to understand the big picture first. Investment banking, finance, health, insurance, no matter what industry, you need to know. Companies are likely to assume that all factors have been considered, including trends in the area discussed above. The more you know, the better you can do something useful with the data.
These 7 factors companies look for when hiring a data scientist. so you need to be prepared for these factors.
I hope you have understood that 7 factors companies look for when hiring a data scientist. If you think that I have missed something which is more important for the company’s point of view then you can tell us in the comment section.
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