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 in the world of business and commerce cannot be understated. The ability to understand the market and curate strategies that will prove profitable is a commendable job. Companies are hiring professionals to leverage the benefits of data science to their advantage. Pursuing a career in data science is a smart move, not because it is trendy and pays well, but because it is the pillar that enables the entire industry to transform. In this article, we have listed the 10 data science jobs that will gain more popularity in 2022.
Data Scientist:A data scientist manages highly complex and voluminous datasets by leveraging machine learning and predictive analytics. To work as a data scientist, the candidates would require to possess efficiency in developing algorithms in facilitating the collation and cleaning of the datasets. A degree in computer science, mathematics, or statistics will act as a bonus!
Machine Learning Scientist:A machine learning scientist requires to research new data approaches and algorithms to be used in adaptive systems, including supervised, unsupervised, and deep learning techniques. They generally go by titles like research scientist or research engineer. The major roles of a machine learning scientist are defining, designing, experimenting using ML, NLP, and computer vision to solve complex problems.
Business Intelligence Developer:These professionals are required to analyse complex databases to find out the latest market trends that can impact business decisions. BI developers have to design, prototype, and manage complex data by using cloud-based platforms. To pursue a career as a BI developer, the candidates need to have a good understanding of data mining, data warehouse design, SQL, and other domains.
Applications Architect: 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 efficient architect will be required to possess expertise in application architecture, whose knowledge can be translated into optimized business operations. The candidates should possess a bachelor’s degree in software engineering, application development, or other areas to gain an edge over the others.
Data Analyst: They are responsible for designing and maintaining data systems and databases including fixing coding errors and other data-related problems. The data analyst uses statistical tools to interpret datasets while paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts. A successful data analyst should possess a combination of leadership and analytical skills.
Statistician:Statisticians work to collect, analyse, and interpret data to identify trends and relationships used to make organizational decision-making. Besides, the regular responsibilities of a statistician often include designing data collection processes, communicating findings to stakeholders, and advising organizational strategy, to name a few.
BigData Architect: Big data architects and engineers create and plan the entire big data environment leveraging Spark and Hadoop systems. To pursue a career as a big data architect The candidates have to be experts in areas like data mining, data migration, and data visualization. Additionally, they will be required to showcase their potentials in Java, Python, C++, and other programming languages.
Machine Learning Engineer: They have to be adept at working with a range of programming languages and should be proficient in AI programming. As machine learning engineers, the candidates will be required to apply predictive models and NLP to manage huge datasets. Experience in ML application development along with proficiency in programming languages like Scala, Python, and Java are some of the common requirements from an ML engineer.
Enterprise Architect: An enterprise architect will be responsible for the upkeep and maintenance of the company’s IT networks and services. They will be required to oversee, improve, and upgrade enterprise services. Additionally, the architects will also be responsible for aligning an organization’s strategy with the technology needed to fulfil it.
Data Science Manager: The data science manager is responsible for helping organizations to leverage the collected data and work with the team of data scientists and engineers to provide valuable insights and direction to the management team. Data science managers are being extensively hired by consulting firms, financial institutions, healthcare organizations, and insurance companies.
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