What is the Work of Data Scientist

Businesses across the globe today understand the value of data-driven insights to inform decision-making and operations. What is the work of data scientist in this equation?

And businesses understand the importance of someone who is very skilled with data and operations. And that certain someone is none other than a data scientist.

In the modern, so-called data-driven world, the need for data scientists is skyrocketing. But what is it really like to be a data scientist?

In this blog post, we will explore what the actual job of a data scientist looks like throughout the day.

Then What is the Work of Data Scientist

Diving Into Data (Morning Routine)

One of the first activities, or what you can call the morning ritual, is to check the progress of ongoing data processes and the results from automated systems that ran overnight. These initial checks help identify any immediate issues that need attention.

At the same time, data scientists pull all the data from various sources, ensuring that all the necessary information is available for the day’s analyses.

Mid-Morning Data Collection & Cleaning

Much of the day of a data scientist is spent collecting and cleaning data. The process of cleaning and collecting data involves extracting data from various sources while making sure it is free of errors.

Data cleaning makes sure the quality of insights drawn from data is as good as the data itself. The task of cleaning and collecting data requires utmost attention to detail and a strong understanding of data sources and types.

Exploratory Data Analysis (Before Lunch)

Exploratory data analysis (EDA) is a method of analysing data sets to summarise their most important traits with the use of visual techniques.

In this process, data scientists use statistical tools like R, Python, or SQL to identify patterns, correlations, and anomalies within the information.

This step leads to formulating hypotheses and identifying the path of extra-designated analyses.

Advanced Analytics & Model Building (Post Meal)

Building and testing predictive models requires the use of machine learning. To know algorithms to examine the data and predict future trends.

Data scientists often use tools like Tensorflow, Scikit-Learn, and others to develop predictive models. The purpose of creating predictive models is to be able to predict outcomes to help businesses make informed decisions.

Communication & Collaboration

The ability to communicate findings to stakeholders who might not have a technical background is one of the key aspects of a data scientist’s job.

This process involves synthesising clear and concise reports, visuals, and presentations that translate complex data insights into actionable business strategies.

Problem Solving & Innovation

The latter part of a data scientist’s day involves tackling even more complex problems that need innovative solutions.

From marketing campaigns to customer satisfaction or enhancing operational efficiency, data scientists are constantly looking for ways to leverage data to solve all these problems.

Salary Of Data Scientist

As you have gone through the routine of Data Scientist, Do you know that average salary of Data scientist in 2024 varies based on experience, location, and specific roles within the field. Below is an overview:

Experience Level	Annual Salary Range (INR)	Average Annual Salary (INR)
Entry Level (0-3 years)	₹3,68,170 – ₹5,11,468	₹3,68,170
Early Career (1-4 years)	₹3,46,000 – ₹7,73,442	₹7,93,400
Mid Career (5-9 years)	₹6,06,000 – ₹13,67,306	₹12,00,000 – ₹14,00,000
Senior Level (10-15 years)	₹10,00,000 – ₹45,00,000	₹15,78,800
Lead Data Scientist (>15 years)	₹24,00,000 – ₹1 Crore	₹18,45,600

Data Scientist Salary Based On Location (City Wise)

City	Average Annual Salary (INR)
Bangalore	₹9,84,488
Chennai	₹7,94,403
Hyderabad	₹7,95,023
Mumbai	₹7,88,789
Pune	₹7,25,146
Kolkata	₹4,02,978

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Conclusion

Being a data scientist today requires being equipped with technical skills, analytical thinking, and effective communication. A data scientist helps organisations figure out the complexities of big data by turning raw information into valuable insights and valuable insights into business strategies.

If you are considering a career in data science, this blog post will help you understand the daily responsibilities and the jobs to be done in a day for a data scientist.