Roles and Responsibilities of a Data Scientist

Data science has significantly evolved the way businesses operate. Nowadays, every business or product interaction is driven by data, creating a huge demand for data scientists. According to the U.S. Bureau of Labour Statistics, the job of a data scientist is projected to rise by 35% until 2032, which is considered the fastest growing rate from the average of all occupations.

By the end of this article, you will gain a clear idea about data scientist roles and responsibilities. Keep reading!

Table of Contents

• Data Science Demand Overview
• Data Scientist Job Description
• Data Scientist Roles and Responsibilities
• Skills to Become a Data Scientist
• How to Become a Data Scientist
• How Much Does a Data Scientist Make?
• FAQs About Data Scientists

Data Science Demand Overview

Data science is one of the fastest-emerging fields in the 21st century. Since the origin of generative AI, companies have started understanding the importance of data and are looking to incorporate data science and AI solutions into their products. Owing to that fact, data science is seeing immense growth and popularity. 

The graph below validates how the number of searches for data science has drastically grown over the years:

Source: Google Trends Report for Data Science Searches Growth

This indicates a growing demand for data scientist roles. According to the U.S. Bureau of Labor Statistics, there will be 11.5 million job vacancies for data scientists by 2030. That’s why the world has gone crazy over data science!

Why exactly are businesses investing heavily in data science? Here’s an interesting report by Forbes that would answer this concern. Forbes reports that data-driven companies are 23 times more likely to perform better than competitors in acquiring new clients, around 19 times more likely to maintain profit, and about 7 times more likely to retain customers.

The staggering demand, positive impact on the business, and popularity are the reasons why data science is a great career option. Do you want to become a data scientist? Then, you must understand what a data scientist’s job role entails. Let’s comprehend that by having a thorough look at the data scientist job description.

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Data Scientist Job Description

A data scientist is an individual who churns data for an organization to produce effective business solutions that would help them improve their offerings, services, and overall business. Ideally, a data scientist starts by gathering data from websites or databases. After that, they apply their statistical skills and machine learning modeling abilities to figure out the trends and make predictive analysis happen. 

The task of a data scientist may vary based on the organization and the specific project. However, here are some of the common tasks they generally perform: 

• They serve as the lead data strategist to identify and integrate new datasets that can be used through product capabilities and work directly with the technical team to develop data products.
• They analyze the data for trends and patterns and then interpret the data with a clear objective.
• Data scientists develop and use algorithms and models to mine big data stores; conduct data and error analysis to improve models; and clean and validate data for uniformity and accuracy.

Now let’s have a look at the roles and responsibilities of a data scientist.

Data Scientist Roles and Responsibilities

The best way to understand the key responsibilities and definition of any job role is by going through multiple job descriptions for that role. To help you understand the wide scope of the data scientist role, we have gone through a couple of data scientist job descriptions on LinkedIn.

Here’s what a data scientist’s job description looks like at big organizations like IBM, VISA, and Ninjacart:

Source: LinkedIn

Source: LinkedIn

Source: LinkedIn

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Based on the above job descriptions, below are the tasks and responsibilities data scientists will undertake:

1. Gathering, Organizing, and Evaluating Data

Large amounts of data are gathered, cleaned, and analyzed by data scientists from various sources. They try to find correlations and patterns among the data to find the trends. The tasks involved in this process include:

• Big Data processing
• Arranging spreadsheet data
• Using Python (Pandas) to create dataframes
• Utilizing R programming language’s statistical packages to examine data

2. Developing Predictive Models

Once the data is gathered and organized by the various processes, data scientists have to build various models depending on the types of data, which can predict the trends in the market. They develop various machine learning algorithms and statistical strategies to build models for gaining better insights from the datasets.
There are various machine learning models, such as clustering models, forecast models, outliers models, etc. Clustering models work by sorting similar data together based on certain features and characteristics. A forecast model is a model that predicts the future based on historical data.

3. Creating NLP and Computer Vision Models

Data scientists collect textual data from various sources and preprocess it by cleaning, removing stop words, and extracting relevant features. Then, they choose suitable natural language processing (NLP) algorithms or models, such as Recurrent Neural Networks or transformers, and train them on preprocessed data.

Similarly, data scientists collect and preprocess image or video data. They use convolutional neural networks, or CNN, to extract visual features. Then, they choose and train computer vision models and assess and fine-tune them before deployment.

4. Fine-Tune Developed Models

As a data scientist, one of the key responsibilities is fine-tuning developed models to enhance the performance of a particular task or database. Initially, the data scientist uses transfer learning to apply the information and features gained from pre-training on a large dataset. Then they use the pre-trained model as a starting point for the desired task. 
During the fine-tuning process, data scientists train the model on a smaller, task-specific dataset to improve its learned representation. By doing this, they help in improving the model’s performance, which allows models to use existing knowledge more efficiently for faster model development.

5. Implementing MLOps Practices

Implementing MLOps is also an important responsibility of a data scientist. They are responsible for the deployment of machine learning models into production environments, which ensures seamless integration with the existing systems or APIs. They also establish CI/CD pipelines for automated model deployment and work on monitoring and maintaining model performance.

6. Creating Data Visualizations

Data visualization is an important tool for data scientists to showcase their findings effectively. This involves various graphical representations such as bar charts, scatter plots, line graphs, and heat maps. This graphical representation helps the end user visualize and interpret the findings.
As a data scientist, you have to choose the tools for data visualization carefully so that you can easily convey the message of the data that has been analyzed. There are various tools used by the data scientist for data visualization, such as Tableau, Power BI, HighCharts, D3.js, etc.

7. Translating Technical Terms into Simple Terms

Many colleagues and clients come from non-technical backgrounds. It is the role and responsibility of data scientists to break down complex technical terms into simple ones using various analogies and explaining the context and purpose of the terms.

Skills to Become a Data Scientist

The job of a data scientist revolves around the gathering, processing, and analysis of large amounts of data to find meaningful insights. To do so, data scientists use their various skills related to computer science, mathematics, and statistics-related concepts.

Here are some key skills required to become a data scientist:

• Proficiency in programming languages such as Python or R
• Expertise in dealing with databases and SQL
• Good command of statistics, probability, and mathematics
• Solid understanding of machine learning principles
• Proficient in database languages, such as SQL and NoSQL
• Skilled in data visualization tools, such as Tableau, PowerBI, etc.
• Familiarity with big data tools, such as Apache Hadoop and Apache Spark
• Understanding of business objectives
• Good analytical and communication skills

How to Become a Data Scientist

Becoming a data scientist can be challenging, but you can achieve it with practice and patience. Here is a step-by-step guide to becoming a data scientist.

Step 1: Acquire a Bachelor’s Degree in Data Science

The very first step is to get a bachelor’s degree.

If you are a college student, you have a great opportunity to opt for a data science subject as a minor. If you are just entering college, you can choose a specialization in data science. Even if you are not from a data science background, you can also become a data scientist, but for that, you need to go for certifications in data science-related fields. 

Step 2: Enhance Your Data Science Skills

Once you are done with the bachelor’s degree, you are equipped with some skills related to data science. However, if you are not from a data science background or if you are from a non-tech background, you must acquire some technical and transferable skills.

Step 3: Get a Data Science Certification

Certification is a credential that will not only help you build your relevant skills, but it will also help you showcase your potential to employers. Since data science is a gaining the reputation in IT industry, taking on a certification program could prove beneficial.

Step 4: Excel in Data Science Tools

Data scientists use various types of data science tools to complete their tasks efficiently. Moreover, the tools they use depend on the specific task they need to perform. So you need to familiarize yourself with tools such as ScrapingBee for web scraping, Tableau for data visualization, Grafana for analytics and monitoring, HighCharts for interactive charts, etc.

Step 5: Start Your Data Science Career

When it comes to a data science career, it is very crucial to plan strategically. You should start by defining your career objectives first, like which data science role you want to go for, which industry you prefer, and the skills you want to develop further.

Now start working on your resume. Try to align your resume according to the job description. Also, build a strong online presence on various platforms, such as LinkedIn, Naukri.com, and Indeed. Once done with this, start preparing for the interview. Now you are ready to apply for the jobs!

How Much Does a Data Scientist Make?

The salaries of data scientists in India and in the USA are provided below in the table according to Glassdoor.

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FAQs About Data Scientists