Top Ten Roles in Data Science

Are you seeking a career in data science? Well, you may be fascinated by the lucrative salaries data science professionals command or might know that data is of utmost importance to companies these days. Its popularity can also be inferred from the fact that many training providers now offer data science courses online to help professionals build a strong foundation in this domain. Moreover, you will find courses like Power BI training or Tableau course dedicated for professionals willing to become well-versed in using various data science tools.

As data science is a multidisciplinary field, you must not be familiar with the job roles that come under it. So, in this article, we have described the top ten job roles in data science that you can pursue. Some of the responsibilities of one role may overlap with others depending on the organization you work with.

Here goes the list!

Data Scientist

A data scientist is an individual responsible for transforming raw data into actionable insights and sharing it with the stakeholders to make more informed business decisions. In some organizations, they begin working right from the data cleaning stage of the data science lifecycle while in others they start analyzing data after the raw data has been transformed into a usable format by the data engineer.

Data Engineer

Raw data collected from disparate sources isn’t ready for analysis as such. The work of a data engineer begins in the initial stages of the data science lifecycle. They contribute their efforts in data warehousing and data cleaning basically, building and maintaining the entire data ecosystem of an organization. They build data pipelines to transfer data to a warehouse, remove any redundant, corrupt, or duplicate values in data sets, and finally convert data into one usable format.

Machine Learning Engineer

Machine learning engineers basically focus on creating ML algorithms and models and assist data scientists who are trying to accomplish statistical and model-building work. They select appropriate data sets, decide suitable data representation methods, perform statistical analysis, and machine learning tests. Based on the test outcomes, they improve the ML models until it starts giving accurate results.

Business Intelligence Analyst

As the name suggests, a business intelligence analyst or BI analyst helps organizations make more informed decisions by using data and other information. They track important performance metrics like sales, market information, revenue, or customer engagement and try to uncover hidden trends and correlations that signal a potential for improving business practices and driving growth. They use various tools like Tableau, Power BI, and Qlik Sense for this purpose.

Data Analyst

Quite similar to a data scientist role, a data analyst is responsible for gathering, cleaning, and studying data sets to help organizations solve various business problems. They first identify the data to be analyzed, collect suitable data sets, clean the data and prepare it for analysis, and finally analyze the data and interpret the results. The types of data analysis usually include descriptive analysis, diagnostic analytics, prescriptive analytics, and predictive analysis.

Data Architect

Data architects are professionals who build blueprints for data management systems. Primarily, they assess an organization’s potential data sources and design a plan to integrate, centralize, protect, and maintain them. They try to address industry requirements by collaborating with IT teams and business leaders and devising an effective data strategy. Their responsibility also includes designing, documenting, constructing, and deploying database architectures and applications.

Database Administrator

As a database administrator, a person basically ensures that the data is available, easily accessible when needed, and secured from any loss or corruption. They collaborate on the initial installation and configuration of a new database, and further take care of updates, patches, and maintenance required. Additionally, they suggest the best hardware devise that meets the company requirement based on factors like cost, efficiency, and performance.

Statistician

You may have heard that statistics is an important topic to learn when starting a career in data science. Earlier playing a crucial role in research and academia, statisticians are now in increased demand in data science field as they are capable of applying statistical methods and models to real-world problems. They collect data, apply statistical and analytical techniques, and uncover trends based on the results of their calculations and projections. Moreover, they ensure data integrity by performing data cleaning, error checking, and validation.

Data Modeler

Professionals who work as data modelers develop conceptual, logical, and physical data models, implement relational database management system, operational data store, and data lakes on platforms SQL and NoSQL databases. They supervise and govern the expansion of existing data architecture and optimize data query performance through best practices. By collaborating with business and application teams, data modelers implement build data flows, data strategies, and develop physical data models.

Computer and Information Research Scientist

Companies hire computer and information research scientists to increase their efficiency in various areas like faster computing speeds, improving information security, and better networking technology. As part of the data science team, they help data scientists and data engineers solve complex computing problems. They explore the fundamental issues in computation and develop theories and models to address those issues.