Data Science

Data Scientist Roles and Responsibilities: Here’s What You Should Know in Today’s Market

Written byKarin Kelley
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Updated onFebruary 15, 2024

Data science is a relatively new field in technology and has been dubbed one of the “sexiest jobs of the 21st century.” It evolved from data mining and statistical analysis to handle the volume of data that businesses collect and process daily, which has increased exponentially in the last few years.

Modern businesses are flooded with data. Each new device added to a corporate network automatically collects and stores information. Every new website or phone app adds even more data. And data scientists are the alchemists who transform this data into insights useful to businesses. In this article, we’ll dig deeper into data scientist roles and responsibilities, what they’re typically paid, and how you can become one through an online data science course.

What Is a Data Scientist?

Data scientists can come from various backgrounds because the skills required in data science go beyond standard computer science or programming skills. The main thing they deal with daily is big data.

They gather, analyze, and transform large amounts of structured and unstructured data from many sources, including website statistics, social media, IoT devices, sensors, and more. Then, they use this data to answer important questions about a business, identify trends, and provide insights to business leaders. They may use statistical analysis or build predictive and prescriptive machine learning models to extract answers from data.

Those are the basics of data scientist roles and responsibilities. Let’s look at the details.

Also Read: What is Data Science? A Comprehensive Guide

Data Scientist Roles and Responsibilities

Data scientist roles and responsibilities vary depending on the company they work for and the industry they’re in, but some of their everyday tasks and duties include:

Data collection and transformation: Data scientists must wrangle disparate data sources and combine, clean, and transform them into a structured, useful format.
Exploratory data analysis: They use statistical techniques and visualization tools to explore and discover patterns and trends in their collected data.
Machine learning and predictive modeling: Data scientists build and train machine learning models that can be used to make predictions and identify patterns in the data.
Data visualization and communication: They create visualizations and reports communicating complex data and insights to non-technical stakeholders.
Collaboration and teamwork: Data scientist responsibilities include working closely with other team members, such as business analysts, software developers, and project managers, to ensure their insights are applied effectively.
Domain expertise: They also need to have a deep understanding of the industry and domain they are working in so that they’ll ask the right questions and identify relevant insights.

Data Scientist Skills

Successful data scientists require a unique blend of technical and soft skills. Here are some of the key data scientist responsibilities and skills necessary for a successful data scientist career:

Programming: They use programming languages like Python or R to collect, clean, and manipulate large data sets.
Mathematics and statistics: A strong foundation in mathematics and statistics is essential for understanding and applying advanced analytical techniques like predictive modeling.
Data visualization: Data scientists create visualizations that explain complex concepts and insights to stakeholders who aren’t as well-versed in technology.
Problem-solving: Part of the data scientist job description is exploration, so they need to be able to get to the root of complex problems and use data to find and suggest a solution.
Domain knowledge: A solid understanding of the industry they are working in enables data scientists to ask the right questions and identify relevant insights.
Communication skills: Data scientists must explain complex data and findings to nontechnical stakeholders and collaborate effectively with other team members.
Continuous learning: The field of data science is constantly evolving, and data scientists need to be committed to continuous learning and professional development to stay up to date with the latest tools and techniques. There are high-quality data science programs available to help professionals achieve this.

Data Scientist Salaries

Data scientists are in demand. According to the U.S. Bureau of Labor Statistics, the job market for data scientists is expected to increase 36 percent by 2031. Because of this demand, these professionals are typically paid relatively well.

According to Indeed, the average base salary for a data scientist ranges from $76,739 to $202,612. And this is only the base salary. Total compensation could include benefits like fully paid health insurance, 401(k) programs, and stock options.

One of the most significant factors impacting salary levels is experience. For example, data scientists with less than a year of experience typically receive an average salary of $105,452 annually.

Location is another significant factor. The highest-paying cities for data scientists in the U.S. include:

Palo Alto, CA: $156,923 average
San Franciso, CA: $139,757 average
Seattle, WA: $136,392 average
New York, NY: $135,824 average
Boston, MA: $135,691 average

How To Become a Data Scientist

Becoming a data scientist typically requires a combination of education, technical skills, and practical experience. Here are some of the steps you can take to become a data scientist:

Earn a degree: Many data scientists have a bachelor’s degree in computer science, mathematics, statistics, or a related field. A master’s degree or higher can also help, particularly for more advanced or specialized roles.
Build technical skills: You must be proficient in programming languages like Python or R and statistical analysis tools like SAS or SPSS. Online data science courses, tutorials, and bootcamps can be a great way to hone these skills.
Get practical experience: Internships or entry-level roles in fields like data analysis or business intelligence can provide valuable practical experience for someone seeking a future in data science.
Develop a portfolio: Turning your data science side projects into a portfolio can display your technical skills and practical experience to potential employers.
Participate in data science communities: Being active in online communities or attending data science events can provide networking opportunities and access to the latest tools and techniques in the field.
Consider certification: A certification that shows you have gained all the skills you need to perform data science tasks professionally can draw the attention of more recruiters.
Continuously learn and grow: The field of data science is constantly evolving, so it’s essential to stay up to date with the latest tools, techniques, and industry trends through ongoing learning and professional development.

Also Read: What Are Today’s Data Scientist Salaries Like?

Are You Interested In a Data Science Career?

Data science is an exciting and rewarding field. Data scientists take massive amounts of data collected from many devices and applications and transform it into valuable business information. The insights they gather can steer businesses toward success and a brighter future. Data scientists are unsung heroes, predicting and guiding the future of business.

If you want to take on data scientist roles and responsibilities in your career, this Data Science Bootcamp is one of the best ways to get started.

In this bootcamp, you’ll complete over 25 real-world projects in three domains, including capstone projects. While doing that, you’ll study data visualization, deep learning, descriptive statistics, exploratory data analysis, and more in supervised and unsupervised learning environments.