Top 10 Data Science Jobs That Will Rise In 2022


by Sayantani Sanyal


November 18, 2021

Data Science Jobs are currently the most sought-after career options among aspiring tech professionals.

Data science encompasses the theoretical and practical application of ideas, including big data, predictive analytics, and artificial intelligence. The importance of data science to business and commerce cannot be understated. The ability to understand the market and develop strategies that will prove profitable is commendable work. Companies hire professionals to take advantage of the benefits of data science. A career in data science is a smart move, not because it’s trendy and well paid, but because it’s the backbone that transforms the entire industry. In this article, we’ve listed the 10 data science professions that will gain popularity in 2022.

Data Scientist:A data scientist manages very complex and large data sets by leveraging machine learning and predictive analytics. To work as a data scientist, candidates will need to be effective in developing algorithms that facilitate the collection and cleansing of data sets. A degree in computer science, mathematics or statistics will serve as a bonus!

Machine Learning Scientist:A machine learning scientist must research new data approaches and algorithms for use in adaptive systems, including supervised, unsupervised, and deep learning techniques. They usually have titles such as scientific researcher or research engineer. The main roles of a machine learning scientist are to define, design, experiment using ML, NLP and computer vision to solve complex problems.

Business Intelligence Developer:These professionals must analyze complex databases to discover the latest market trends that can impact business decisions. BI developers must design, prototype, and manage complex data using cloud-based platforms. To pursue a career as a BI developer, candidates should have a good understanding of data mining, data warehouse design, SQL, and other areas.

Architec applicationst: An application architect oversees the design and development of software applications. They collaborate with internal stakeholders and application development teams to implement and monitor application development stages and document the application development process. An effective architect will need to have application architecture expertise, the knowledge of which can be translated into optimized business operations. Applicants should have a bachelor’s degree in software engineering, application development or other fields to stand out from the crowd.

Data analyst: They are responsible for the design and maintenance of data systems and databases, including correcting coding errors and other data-related issues. The Data Analyst uses statistical tools to interpret the data sets while paying close attention to trends and patterns that could be useful for diagnostic and predictive analysis efforts. A good data analyst should have a combination of leadership and analytical skills.

Statistician:Statisticians work to collect, analyze and interpret data to identify trends and relationships used to make organizational decisions. In addition, the usual responsibilities of a statistician often include designing data collection processes, communicating results to stakeholders, and advising on organizational strategy, to name a few.

FatData architect: Big Data Architects and Engineers create and plan the entire Big Data environment using Spark and Hadoop systems. To pursue a career as a Big Data Architect Applicants should be experts in fields such as data mining, data migration, and data visualization. In addition, they will have to show their potential in Java, Python, C ++ and other programming languages.

Machine learning engineer: They must be able to work with a range of programming languages ​​and must be proficient in AI programming. As machine learning engineers, candidates will need to apply predictive models and NLP to handle huge data sets. Experience in developing ML applications as well as proficiency in programming languages ​​such as Scala, Python, and Java are common requirements for an ML engineer.

Enterprise architect: An enterprise architect will be responsible for the upkeep and maintenance of the enterprise’s IT networks and services. They will be required to oversee, improve and upgrade corporate services. In addition, architects will also be responsible for aligning an organization’s strategy with the technology needed to achieve it.

Head of Data Science: The Data Science Officer is responsible for helping organizations leverage the data collected and working with the team of data scientists and engineers to provide valuable information and guidance to the management team . Data science managers are widely hired by consulting firms, financial institutions, healthcare organizations, and insurance companies.

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