Recruiter’s Viewpoint While Hiring a Data Scientist

With everything going digital, organizations are collecting a huge amount of data and hence the demand for data scientists has gained new heights. As someone said, “data is the new oil”, which is a very true statement. Like the real crude oil needs to be processed to get the most out of it. Similarly, data also needs to be processed to convert it into useful information. Without any modification, storing raw data is a waste of time and resources.

Data Scientists are in demand because of their skills in integrating the knowledge of computers, statistics, and mathematics to analyze and model data and then interpret it to make a feasible decision to maximize the profit of the company.

But, hiring a data scientist is not an easy job for a recruiter because nowadays everybody is following the trend by learning different pre-existing classifiers and regression models instead of getting the concept behind them.

Who are Data Scientists?

In simple words, the work of a data scientist is to extract useful information from messy and unstructured data from different sources like social media feeds, surveys, historical data, etc.

They are the experts who use their technological and social science skills to find trends in the market and provide feasible solutions to an organization.

In the industry, Data Scientists have to handle huge data, so one should have exceptional mathematical skills along with programming skills.

What Does a Recruiter Expect from Data Scientists?

Here are the essential skills that a data scientist recruiter expects in a data scientist’s resume -

1. Technical Skills

This section is all about a data scientist’s technical skills required such as how good they are at programming, data structures, etc.

He/She should have good experience in programming using Python or R. Apart from these, he /she should have worked with other languages like C++, Java, etc.

They should know different frameworks and libraries of python like Pandas, NumPy, TensorFlow, Kera's, etc. and most importantly they should be good with data management tools like Excel, Tableau, etc.

2. Find Patterns in Data

Just because someone has a degree in the field of data science, it doesn’t mean that they are also good at finding patterns in data. Candidates should have an intuitive approach to the dataset.

Since the role and work of Data Scientists are constantly evolving they can make decisions that should be optimal for the organization and customers in certain circumstances. Data scientists with this quality can find patterns even in an unstructured dataset.

3. Statistical Knowledge

Without statistical knowledge, the future of data scientists is in dark. The recruiter expects candidates to generate a hypothesis about how the machine will behave if data have variation, what sort of assumption they should make to handle missing values.

Therefore, making accurate conclusions from the dataset is almost impossible without having deep knowledge of mathematics, and statistics.

4. Data Visualization Skills

Generally, people can understand data presented in a pictorial form (graphs, lines, etc.) rather than in tabular form. So if a data scientist wants to present data to a non-technical audience like Product Managers, they have to present it in the form of graphs and pictures for which they must be good at visualizing skills as well as tools like Matplotlib, Plotly, sisense, etc.

5. Knowledge of Linear Algebra and Multivariable Calculus

Though the recruiter may not ask these things in the interview, when it comes to building implementation models in-house, vast knowledge of linear algebra and calculus is required.

In the interviews of an organization whose sole work is in the field of data science. One can expect a recruiter to throw a calculus question at him.

6. Data-Driven Decision Making

Data-driven decision-making is defined as using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives, and initiatives.

Data scientists can not make decisions without adequate data available. To remove anomalies in data, they should ask relevant questions. Their approach should be like a 5-year-old kid who is eager to explore his surroundings and ask the minutest questions.

7. Data Modelling

Last but not the least, they should be good at modeling the data. Because the hardware resources are limited and performing even a simple query on a big database can take huge amounts of system resources.

Therefore, they should be highly efficient while using system resources. The candidate should know how to model the data so that it gives accurate results and at the same time it is less resource-consuming.

Conclusion

Though the demand for data scientists has grown manifolds in the past decade. But at the same time competition has also increased. To get selected as a data scientist in a reputed firm one has to gain skills by doing some real-world projects, earning a degree/diploma in the field of Data Science.

Also, you should have in-depth knowledge of what’s happening in the backend when you are using some classifiers, regression methods.

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