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Data science has grown from a few scattered seeds to a thriving forest in these couple of years. Every second, approximately 1.7 megabytes of new knowledge is generated for every person on the planet, and the Artificial Intelligence (AI) market is expected to exceed $100 billion by 2025. Thus, it’s helpful to hire Data Scientists! 

According to Glassdoor and LinkedIn’s reports, data scientist jobs are the second-fastest-growing career option, with 6.5 times more members citing it as their occupation. 

Are you looking out to hire Data scientists for your organization, then, read our guide, which covers everything from the most important qualifications to what to look during the data scientist interview test? 

What is the role of a Data Scientist? 

The role of Data scientists is to collaborate closely with business stakeholders to learn about their objectives and how data may help them achieve it. They need to construct algorithms and prediction models to extract the data that the business need, as well as help, evaluate the data and share findings with peers. 
 

Some of the key roles and responsibilities of Data scientists include:  

Management: The Data Scientist has a minor managerial position in the Data and Analytics area, where he helps to build a foundation of futuristic and technical talents in order to assist with various planned and ongoing data analytics projects. 

Analytics: The Data Scientist creates economic and statistical models for a variety of issues, such as classification, projections, pattern analysis, clustering, sampling, simulations, and more. 

Strategy/Design: The Data Scientist plays a critical part in the development of creative strategies to better understand and manage the company's consumer trends and management, as well as approaches to tackle complex business problems such as product fulfillment and overall profit optimization. 

Collaboration: The Data Scientist's position is not a solitary one, and he works with other data scientists to communicate problems and discoveries to key stakeholders in order to improve company performance and decision-making. 

Knowledge: The Data Scientist also takes the lead in experimenting with new technologies and tools in order to develop novel data-driven insights for the business at the fastest possible pace. They also take initiative in evaluating and implementing new and improved data science methodologies for the company. 

Also Read: The Rise And Rise Of Data Science 

Why Is It Essential to Hire Data Scientists Based on Python Track?  

What Are The Key Essential Python Data Science SkillsPython is one of the most commonly used programming languages in the field, with the majority of data scientists using it. As a result of its popularity, Python offers a huge data of libraries and frameworks, which is a terrific addition to your development process. They can easily replace the entire solution and save a lot of manual work. 

Python is a free, open-source programming language, making it an excellent option for beginners. It is an interpreted high-level programming language and has a huge community, that allows for rapid development and can interface with high-performance Fortran or C algorithms. 

Some of the key benefits of hiring employees with python data science skills are: 

  • Helps to incorporate statistical code into production databases. 
  • Helps to integrate data with web-based applications. 
  • Helps in managing machine learning tasks with Python’s sci-learn-kit. 
  • Helps as a perfect solution for graphics and visual-based data-science projects.  
  • Helps to access thousands of data analysis and data science libraries.  

What Are the Key Essential Python Data Science Skills? 

If you are looking to hire a data scientist or one with Python data scientist skills? Then there are five essential skills which being an employer should look for while hiring python data scientists. 

Data Scraping: 

Data can be gathered from blogs, which is one of the most logical and open data sources. To make managing web requests and data formats simpler, you'll need to learn how to use Python packages like urllib2, requests, simple Json, regular expression operations, selenium, and beautiful soup. 

SQL: 

You'll need to learn how to transform raw data into actionable insights, and you'll want to store and process a vast amount of organized data once you have it. You should be able to manipulate and extract data from relational databases using SQL to be an efficient data scientist or engineer. 

Frames of data: 

SQL is useful in data science and is excellent for dealing with vast volumes of data, but it lacks Machine Learning and Data Visualization. As a result, you'll either have to go through the painstaking process of allowing Machine Learning services in SQL Server or use MapReduce to reduce the size of the data before processing it with Pandas. 

Mathematics & Statistics: 

An applicant for the role of data scientist should be well-versed in a number of mathematical concepts. Topics covered include descriptive and inferential statistics, linear algebra, probability, and differential calculus. 

Data Visualization:  

Data visualization is more of an art than a pre-programmed procedure. A Data Visualization specialist understands how to use visualizations to tell a story. To begin, you must be familiar with basic plots such as histograms, bar charts, and pie charts, before moving on to more advanced charts such as waterfall charts, thermometer charts, and so on. During the exploratory data analysis level, these plots are extremely useful. Colorful maps make univariate and bivariate analyses much easier to comprehend. 

Machine Learning Algorithm:  

Machine learning areas and techniques include neural networks, reinforcement learning, adversarial learning, etc. Learn supervised machine learning, decision trees, logistic regression, and other machine learning techniques to set yourself apart from other data scientists. These abilities will assist you in solving a variety of data science problems that are based on important organizational result forecasts. 

Also Read: Four Quick Steps to Lead Upskilling Initiatives for Data Science Talent 

So, these are some of the most important top python data science skills which you being a recruiter should definitely give a try before hiring data scientists. Also, to ease up and streamline your entire skills assessment process to hire a Data Scientist with Python test, here’s how the iMocha - skill assessment platform is to help.   
 

  1. Hire within a shorter time frame 

 
Data discovery, data analysis, data pre-processing, model development, model training, and testing are just a few of the tasks involved in solving a real-world Machine Learning problem. As a result, assessing candidates' abilities on real-world issues will take a long time. As a result, in order to test candidates' expertise, our platform provides a series of estimated python data science interview questions in which large databases are broken down into smaller ones so that candidates can demonstrate their abilities within the time limit. It also aids hiring managers in narrowing down applicants for more in-depth assignments or even deciding on candidates for entry-level positions. 
 

  1. Extensive data sets are used to conduct online data science test  

 

You being a recruiter may use the developer evaluation tool to evaluate candidates' abilities on real-world Machine Learning issues. But, iMocha’s data scientist interview test help to better assess applicants before they move on to the next round of interviews or before the final offer is made. Recruiters will also get a summary of the test and also track the success of all applicants and currently participating participants, as well as shortlist candidates, using the platform.  
 

Conclusion:  

So, it concludes our information about how to hire Data Scientists. Some of the most important points to remember are: 

  • It is important to have the necessary skills. You shouldn't go far wrong with a hire if you have the skillset (or ‘potential' with those skills) correctly. 
  • Keep an open mind within your boundaries because a great Data Scientist may have been several different things before, they walked through your door. 
  • Work with an appraisal tool so you don't have any doubts at the end of the recruiting process. 

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Laxmikant Kharangate
Laxmikant Kharangate
Laxmikant Kharangate is a Quality Analyst at iMocha with expertise in data science, machine learning, and artificial intelligence. As a QA, he focuses on making sure that our customers are happy, providing best practices to ensure they find efficiency in technical hiring. He loves reading technical articles, and plays cricket and explores photography in spare time.
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Topics: Tech Recruitment

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