Top 10 Data Science Career Options That are on The Hype

by November 23, 2020 0 comments

Data Science

According to a report, data related firms post 2020 are anticipated to add around 1.5 lakh new openings

Data science is one of the most appealing industries with a lot of features and opportunities. With humans using 2.5 quintillion data per day, the data landscape is at a dynamic space, almost mimicking the real global connectivity. New technologies to tackle data overwhelming are introduced year after year and the transformation is likely to continue into the coming decade. The rise for data-related practitioners in the fast-moving world is very real. According to a report, data related jobs post 2020 is anticipated to add around 1.5 lakh new openings. For the past four years, data scientist has been named the number one job in the US by Glassdoor. The US Bureau of Labour Statistics reports that the demand for data science skills will drive a 27.9% rise in employment in the field through 2026. Henceforth, Analytics Insight brings you a list of data science jobs that are on the hype.

 

Data Scientist

Average salary: US$139,840

Data scientists help in performing data preparation tasks like cleaning, organizing and many more that allows companies to take strategic actions. They handle large datasets and uncover useful patterns and trends in data. Data scientists are technological persons who are fluent in data analysis software and use them to predict market patterns. Data scientists practice advanced analytic technologies such as machine learning and predictive modeling which are examined through enormous amounts of structured, unstructured, and semi-structured data to identify definite patterns. The opportunities for data scientists with more skills are expected to rise in future.

 

Data Analyst

Average salary: US$62,453

Data analyst is one of the promising career options in the data science field. Data analysts transform and manipulate large data sets. They also assist higher-level executives in gleaning insights from their analytics. A data analyst is answerable for tracking web analytics, analyzing A/B testing, operating and altering large datasets to be lined up with anticipated analysis for businesses. They also work jointly with the management to develop a priority-based list of corporate and data requirements for their projects. Data scientists are already in huge demand in the data science landscape.

 

Machine Learning Engineer

Average salary: US$114,826

Machine learning engineers are responsible for creating data funnels and delivering software solutions. Besides, they are also responsible for exploring and applying suitable machine learning algorithms and tools. By learning and transforming data science prototypes and picking appropriate datasets and representation techniques, they also design machine learning systems from the beginning. Machine learning engineers often use other technologies like deep learning and artificial intelligence to create automation in data analysis.

 

Data Engineer

Average salary: US$102,864

Data engineers perform batch processing or real-time processing on gathered and stored data. Data engineers share a cooperative relationship with data scientists. They are responsible for making the data understandable and readable for the data scientists. Data engineers manage the development, construction, maintenance and testing of architectures such as datasets and large-scale processing systems.

 

Machine Learning Scientist

Average salary: US$114,121

Machine learning scientists take over the task of researching new approaches such as algorithms, supervised and unsupervised learning techniques. Machine learning scientists often go by titles like Research Scientist or Research Engineer.

 

Data Architect

Average salary: US$108,278

Data architect creates new database systems, use performance and design analytics to improve the interconnected data ecosystem within the company. They are responsible for developing data solutions for multi-platform presentation and design analytics applications. They confirm with company policies and agreement and external rules, maintaining the integrity and security of the company database by investing database application approaches carefully. With the rise of automation in data science, data architects are in huge demand to provide better solutions.

 

Business Intelligence Developer

Average salary: US$81,514

Business Intelligence Developers are considered as one of the most wanted data science professionals in the corporate world. They are responsible for designing and developing policies which help the organization in making better business decisions. In place to make the understanding of system procedures easier, a business intelligence developer can either utilize current BI analytic tools or build their own tools. Besides, the developers are also responsible for regularly developing and improving IT solutions by coding, designing, testing, debugging and implementing such tools.

 

Statistician

Average salary: US$76,884

Statisticians are hired to collect, analyze and interpret data, thus assisting the decision-makers with their work. They identify trends and relationships which can be used to inform organizational prospects. Additionally, the daily responsibilities of statisticians often include design data collection processes, communicating findings to stakeholders, and advising organizational strategy.

 

Enterprise Architect

Average salary: US$110,663

Enterprise architects engage in aligning the company’s strategy with technological solutions. They are responsible for managing the creation, improvement, preservation and management of IT architecture models and support systems. Enterprise architects also work with stakeholders, subject matter experts, and management.

 

Infrastructure Architect

Average salary: US$107,309

Infrastructure architect oversees the existing business systems to ensure that they support the new technological requirements. They also help in cost optimization. They guarantee that their firm has the necessary tools for analyzing big data.

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