Businesses are becoming more and more data driven. They leverage data for almost all business decisions – causing a boom in demand for data science and related roles. According to LinkedIn, the data science field has seen a 650% job growth since 2012. 

This blog will cover the best data science jobs you can land with a data science education or with good (preferably practical) experience in data science subjects. 

The Hottest Data Science Jobs in 2022

Here are the most in-demand and high-paid data science roles: 

1. Data Analyst

A data analyst collects, cleans, and interprets data sets to solve a problem or answer a question. They work across industries like business, finance, criminal justice, science, medicine, and government.


  • Removing corrupted data – fixing coding errors and related problems
  • Utilizing tools to extract data from primary and secondary sources
  • Developing and maintaining databases, data systems – reorganizing data in a readable format 
  • Analyzing data to assess quality and meaning 
  • Filtering data by reviewing reports and performance indicators
  • Using statistics to identify, analyze, and interpret trends and trends in complex data sets 


A bachelor’s degree with an emphasis on statistical and analytical skills, like mathematics or computer science. Certifications and bootcamps can offer entry-level data analyst jobs. However, a master’s degree can lead to senior positions. 

2. Data Scientist

Data scientists are of foundational importance for most data-related projects. They offer teams an understanding of what data types to use, what data transformations must happen, and how they will be applied in the future. 


  • Deploying data techniques to find solutions to business problems
  • Identifying proper data sets and variables needed to research an issue.
  • Collecting large data sets from various sources
  • Collaborating with IT department and business teams
  • Looking for patterns and trends in data that could affect the business’s direction


At least a bachelor’s degree in a relevant discipline, such as Business information systems, Computer science, Economics, Information Management, Mathematics, and Statistics. A Master of Science in data science degree can help you land-senior level roles. 

3. Data Engineer 

Data engineers build and maintain data systems. They collect raw information from multiple sources to create a uniform and machine-readable format. They also develop and test architectures that enable data extraction and transformation for predictive or prescriptive modeling. 


  • Develop, construct, test and maintain architectures
  • Use large data sets to address business issues.
  • Deploy analytics programs, machine learning and statistical methods
  • Prepare data for predictive and prescriptive modeling.
  • Use data to discover tasks that can be automated.
  • Deliver updates to stakeholders based on analytics


At least a bachelor’s degree, preferably a master’s in computer science, engineering, applied mathematics or other related IT fields. Since this role requires heavy technical knowledge, bootcamps or certifications alone won’t cut it.  

4. Enterprise Architect

An enterprise architect is different from a data architect. A enterprise architect’s responsibility is over-arching across the enterprise, like upkeeping an organization’s IT network and services. In contrast, a Data Architect has a subset of the enterprise architect’s responsibilities, focused mainly on the data itself. 


  • Finding ways to improve our IT department’s functions 
  • Creating business architecture models that reflect our strategies and goals
  • Evaluate systems engineering, talent recruiting and accounting models for vulnerabilities
  • Reduce IT costs
  • Increase employee knowledge and skills


An undergraduate degree in computer science and 5 to 10 years of IT experience. Some companies may ask for a master’s degree. 

5. Machine Learning Engineer

An ML engineer focuses on researching, building, and designing self-running AI systems to automate self-learning, predictive models. In SMBs, ML engineers often double as data scientists, but in larger companies, both professionals collaborate to provide clean data and create optimal machine learning systems for data scientists to then utilize it to deliver needed data. 


  • Exploration and data visualization.
  • Supervision of data acquisition processes.
  • Feed data into models defined by data scientists. 
  • Define validation strategies.
  • Interpretation of business objectives and development of models.
  • Use of evaluation strategy and data modeling to predict unforeseen instances. 
  • Management of resources available to the machine learning scientist such as hardware and personnel.


At least a master’s degree, and sometimes a Ph. D. in computer science or related fields. Knowledge of mathematics and data analytical skills is crucial. Some employers may ask for adequate work experience. 

6. Decision Scientist

Don’t confuse this with a data scientist’s role. Decision scientists know how to build models, design algorithms, conduct analysis, and present the results. They are grounded in the fields of systems analysis, applied probability, and decision theory. A decision engineer is a Bayesian.


  • Focuses on finding insights and relationships via statistics. 
  • Choose the type of analysis, visualization methods and behavioral understanding to help stakeholders make decisions. 
  • Collaborate with decision-makers and management to offer data-driven insights.


Earn a bachelor’s degree in IT, computer science, math, business, or related field to get junior-level roles. A master’s degree with work experience can help you get senior-level roles. 

How’s The Pay?

Here’s an infographic by Flatiron School, on how different data science roles pay:

Kickstart Your Career in Data Science

Three important steps: 

  1. Determine if you have the right skills: People who want a career in data science should have a solid grip on Statistics, Big data, Software engineering, Deep learning, Data visualization, Programming, Data analysis and manipulation, etc. 
  2. Gain practical experience: Participate in data science projects, internships, bootcamps, and build a portfolio. 
  3. Get a data science degree: Certificates and bootcamps can offer entry-level jobs in some roles. A bachelor’s degree is the safest option for all roles. And a master’s can offer maximum opportunities, senior-level roles, and the highest payouts.

We hope you found this article helpful, for more such content stay tuned to 


Sumit is a Tech and Gadget freak and loves writing about Android and iOS, his favourite past time is playing video games.

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