Today companies rely on data to make an informed decision that will decide the company’s fate. And making that decision requires numerous steps right from collecting to filtering to analyzing data. And at each point, data science professionals have important roles to play. So, if you are someone looking to make a career in Data Science then you must be aware of different data science job roles available in the market. Here, let’s learn how you can start as a junior-level data engineer and progress in your data science career to become a senior Data Scientist which infers reaching the director’s position in the company.
As per Glassdoor, the salary of an entry-level data scientist is $97,167 per year which goes up to $1, 21,066 per annum for Data Scientists and $1, 48,214 per annum for a Senior Data Scientist. Careers in data science are not only lucrative but offer a great learning experience throughout the career. With companies relying heavily on data, the demand for junior, mid, and senior data scientists is increasing rapidly. Looking at the various data science jobs mentioned below, you can easily decide how to go about your career path in data science.
A data analyst is responsible for analyzing and interpreting data to identify patterns and trends. Their main role is to collect, process, and perform statistical analyses on data, and then present their findings to stakeholders in a clear and understandable manner.
Machine learning engineers are responsible for designing, developing, and deploying machine learning models into production systems. They work on the development and deployment of models that can analyze data, learn from it, and make predictions based on new data.
Data engineers are responsible for designing and building data pipelines that extract, transform, and load data from various sources into a data warehouse or data lake. They are also responsible for maintaining and optimizing these pipelines to ensure that they can handle large volumes of data and run efficiently. They often use technologies like Apache, Spark, Kafka, or Hadoop.
Business Intelligence Analysts are focused on analyzing and visualizing data to provide insights that can help their organization make better decisions. They typically work with structured data from internal systems and use tools such as SQL, Excel, and Tableau to develop reports and dashboards.
Data Scientists, are focused on using statistical and machine learning techniques to extract insights from data, often working with larger and more complex data sets that may come from a variety of sources. They typically use programming languages such as Python or R, as well as specialized machine learning libraries and tools such as Tensor Flow, to develop predictive models and algorithms.
Data Architects and Administrators are responsible for managing and maintaining an organization's data infrastructure. They are responsible for developing data governance policies and procedures to ensure that data is accurate, secure, and accessible to those who need it. Data Administrators are responsible for managing the day-to-day operations of an organization's data systems.
Data Science is a booming industry with a projected growth rate of 36% till 2031, making it the fastest-growing occupation in the world. Candidates looking to make their career in data science, have plenty of opportunities available. But the market is highly competitive as well. Therefore, students and professionals need to stay ahead of the competition by upskilling and upgrading. Here are a few tips to give you that extra edge:
Doesn’t matter where you begin in your career, you can amplify your data science career manifold. It is highly recommended that candidates and professionals remain updated with the latest industry trends in the field of data science as the technology keeps on changing every now and then. Getting data science certification will help them learn the latest technologies and trends.
