Top Data Science Jobs & Roles for 2023: Find What Suits You Best

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“Data Scientist, the sexiest job title for the 21st century” 

If you have ever witnessed a discussion on data science jobs and the job roles, you have mostly heard the term ‘data scientist’. But is it the only job profile associated with data science? No, there are many sub-roles that are driving industries of the world.

Today, I will share the various other data science jobs with their roles to help you choose the right one.

Top Data Science Jobs

Top Data Science Jobs

Here are some of the top Data Science jobs with their roles, responsibilities, and salaries. You will also find the details of skills required for Data Science jobs. Let’s start with the most popular one – Data Scientist.

1. Data Scientist

Data Scientists are analytical experts who are responsible for finding insights and patterns in the data. A Data Scientist is responsible for handling raw data, analyzing the data, implementing various statistical procedures, visualizing the data and generating insights from it.

He/she churns raw data and transforms it into meaningful products. A Data Scientist is also responsible for handling both structured and unstructured information.

A Data Scientist must have knowledge of various tools like Hadoop, R, Python, SAS, etc. Knowledge of data preprocessing, visualization and prediction are some of the important requirements of a Data Scientist.

2. Data Architect

A Data Architect is responsible for implementing the blueprints of a company’s data platform. This blueprint or architecture delineates various models, policies, rules that govern the storage of data as well as its use in the organizations.

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A Data Architect is responsible for organizing and managing data both at the macro level as well as the micro level.

Some of the important tools used by a Data Architect are XML, Hive, SQL, Spark and Pig. The average salary of a data architect is $123,680 per annum.

3. Data Engineer

A Data Engineer is responsible for building big data pipelines and models for the data scientists to work on. Data Engineering involves the knowledge of various data-related topics as well as knowledge of software engineering principles.

A Data Engineer must be well versed with both structured as well as unstructured data. A Data engineer is not only responsible for building data models but also maintaining, managing and testing it.

Knowledge of database models and ETL are two of the most essential requirements for a Data Engineer. A Data Engineer is responsible for modeling large-scale processing systems using tools like SQL, Hive, Pig, Python, Java, SPSS, SAS etc.

4. Data Science Manager

A Data Science Manager is responsible for handling and managing data science projects. A Data Science manager handles the team and manages the performance to meet project deadlines.

Usually, data science managers have an average of five-year experience in any of the data science domain like date engineering, data science or analysis.

Data Science managers are responsible for planning and curating a roadmap for the data science team to follow. Furthermore, they are responsible for executing the plan of action and delivering the results before the deadline.

He/She should also have strong communication and leadership skills in order to guide the team efficiently. The average salary for a data science manager is – $69,059/yr.

5. Statistician

A statistician is the oldest job title among all the roles discussed in this blog. Before data science, statisticians were employed by the companies to use statistical modeling for understanding various trends in the market.

A statistician is responsible for implementing A/B testing, harvesting data, describing data, developing inferential statistical tools and performing hypothesis testing.

Some of the tools used by statisticians are R, SAS, SPSS, Matlab, Python, Stata, SQL etc. The average salary of a Statistician is $82,477/yr.

6. Machine Learning Engineer

A Machine Learning Engineer is responsible for tailoring machine learning models for performing classification and regression tasks. A Machine Learning Engineer has the knowledge of various techniques like clustering, random forest and several other deep learning algorithms.

It is an advanced field and people are required to possess analytical aptitude skills to develop machine learning algorithms.

Some of the popular tools used by the machine learning engineers are TensorFlow, Keras, PyTorch, scikit-learn, Caffe etc. The average salary of a machine learning engineer is $114,826.

7. Decision Scientists

The field of decision science is a relatively new field. Decision Scientists help the company to make business decisions with the help of tools like Artificial Intelligence and Machine Learning.

It is a part of data science that extends to design thinking and behavioral sciences to better understand the clients. The average salary of a decision scientist is $69,192/yr.

8. AI Ethics Specialist

An expert in AI ethics works to ensure that AI systems are created and implemented responsibly and ethically, given the growing significance of ethical issues in AI and data science.

9. Big Data Engineer

A big data engineer is a specialist in charge of creating, constructing, and managing infrastructure and large-scale data processing systems for handling enormous amounts of data. They are essential in helping businesses handle, store, and analyse enormous volumes of structured and unstructured data quickly and effectively. Big Data Engineers handle the full data lifecycle, from data intake through data storage and processing, assuring data availability and accessible for data analysts and data scientists. They do this by utilising a variety of technologies and techniques.

10. Machine Learning Engineer

A specialised position in the fields of data science and artificial intelligence (AI) is that of a machine learning engineer. In order to address practical issues and provide AI-powered solutions, machine learning engineers are in charge of creating, developing, and implementing machine learning models and systems. They develop machine learning pipelines that can handle data, train models, and generate predictions by combining data science, software engineering, and domain knowledge.


We went through some of the important roles of data scientists and their salaries. While data science is a wide field, there are numerous opportunities available in this field. In the end, we conclude that data science is a thriving field that holds lucrative salaries and an ample amount of opportunities.

After sharing the details of Data Science jobs and career, I have something more interesting for you –

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