Why hire a Data Scientist for your team – Explore the essential benefits

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Why hire a Data Scientist for your team?

The results of Data Science are already working their magic in our day-to-day lives. Take for instance the Internet, there are more websites than one can imagine – a single google search shows 1 billion results which is a figure that is actually hard to imagine.

Every time you use Google to search for something, a data scientist is working in the background to sift through those 1 billion websites in order to bring you the information you want.

Companies have huge amounts of data (some may have not realized yet!). That is of no use if companies do not hire data scientists who can analyze that data and bring them useful insights.

These insights help organizations to make accurate decisions about the changes needed in the company related to the products and services they offer.

McKinsey (management consultant and professor) predicts that the demand for data scientists is increasing very quickly in the coming future, leading to a 50 percent gap in the supply of data scientists versus demand. But why would companies need data scientists?

You must learn about the Top Data Science Skills before proceeding ahead

Benefits of Having a Data Scientist for Team

Now, in the article on need of data scientist for the team, we will see the advantages of having a data scientist.

Why hire data scientist for your team

  • Helps management to make better decisions – An experienced data scientist is likely to act as a trusted advisor to the company’s upper management. He communicates and demonstrates the value of the data that he collected and analyzed to facilitate the improved decision-making process.
  • Helps to define organizational goals – A data scientist examines and explores the company’s data. After which they suggest and specify certain actions that will help to improve the company’s performance, better engage customers, and ultimately increase revenue generation.
  • Helps the staff to adopt the best practices – One of the tasks of a data scientist is to make the staff aware of the company’s analytics product. They prepare the staff for the effective use of the system to extract meaningful insights and take actions. Once the staff understands it, then they start to address the key business challenges.
  • Helps to identify opportunities – Usually identifying opportunities that will lead to the success of the company is a difficult task to do and surprisingly data scientists are the one who excels in this. This is one of the best advantages of having a data scientist in your team. During an interaction, they question existing methods and start making assumptions for the purpose of developing better processes.
  • Tests the decisions taken – Decisions are taken on the basis of the analyzed data and certain changes are implemented in the organization. It is very important to know how those decisions have affected the organization. Data scientists test if the decisions implemented performed to the level of expectation.
  • Helps to identify the target audience – Most companies have at least one source of customer data that is stored. The customer information is very useful in identifying demographics and hence finding potential customers. A data scientist can help with the identification of the key groups of customers with the help of a thorough analysis of data. This also helps organizations to personalize services and products for customer groups and increase the profit margins.
  • Helps to find the right candidate for the organization – With the amount of information available in hand through social media, corporate databases, and job search websites, data scientists are able to work their way through all these data points to find the candidates who best fulfills the organization’s needs. Data scientists mine the vast amount of data that is available, process various resumes, and make accurate selections.

Data Scientist Daily Tasks

These are the daily tasks that data scientist perform:

1. Data Cleaning

In the business world, incorrect data can be costly. Many companies use customer information databases that record data related to contact information, addresses, and preferences.

For instance, if the addresses are inconsistent, the company will suffer the cost of resending emails or even end up losing customers.

For these reasons, it is very important to have step-by-step guidelines that walk through the quality checks to be applied. There’s a lot of data out there, but much of it is not in an easy to use format.

This part of a data scientist’s job involves making sure that data is nicely formatted, all the errors and duplicates are eliminated, and the irrelevant information is discarded.

2. Data Analysis

This is the sort of work most people think of using Excel for and frankly speaking, it is true but for small businesses only! A data scientist will typically work with large data sets that are unable to open in a typical spreadsheet program even on a single computer.

Data analysis is the stage where you make plots of the data in order to understand it. This enables the data scientist to explain the data in a way that will be easy to communicate and easy to make decisions on.

For example – Data scientists at Facebook figured out that having a minimum of ten friends in a profile helps guarantee that a user will stay active on the site. This is the reason why there are suggestions to find/add more friends to the site.

Do you know – How Facebook uses Data Science

3. Predictive Modeling/Statistics

Depending on the background of a data scientist, they call themselves modeler or statistician. Those who studied statistics, consider themselves statistician and everyone else claims to be a modeler.

After you are done with cleaning and analyzing the data, you would want to make predictions from the data. A data scientist actually spends a lot of time evaluating and twisting the models. If that doesn’t work, he turns back to the data to bring new details that can help make better models.


Data scientists can add value to all businesses who can use their data well. From modeling and insights across the workflows and hiring the right candidates, to helping top-level management make better decisions, data scientists are the most valuable to any company in any sector.

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