or the past four years, data scientists have consistently been named the number one profession in the U.S. Also, a report by the U.S. Bureau of Labor Statistics states that the demand for data science skills will drive the job outlook by as much as 27.9% by 2026. However, with this huge demand, the supply isn’t proportional. There is a short supply of qualified data scientists.
Data is an integral part of technology, and hardly would you work in tech without dealing with data. Your Facebook interactions, Amazon purchases and saves, Netflix, and other services use data to ensure you get the best or, in most cases, relevant services. Amazon is an excellent example of how data collection, analysis, and mining are essential for the average shopper. By keeping track of what you search for, pay for and save for later, Amazon can customize its homepage, other pages, and offers to fit your needs.
The benefits of data science extend beyond helping you save money or make valuable purchases. Through data science, health tech companies can collect data from wearable trackers, encourage the adoption of healthier lifestyles and inform its users of critical health issues. This was implemented during the outbreak of the Ebola virus.
With these innumerable advantages of data science, it is evident that more people need to venture into the field. However, there are many career paths in data science, and these are some of them.
Data Scientist
The day-to-day job requirements of a data scientist are to find, clean, and organize data for the organization. As a data scientist, you need to be able to analyze large amounts of raw data as well as processed information. This analysis aims to find and recognize patterns that will be beneficial in driving core and strategic business decisions for the organization. The average starting salary for a data scientist is $117,212 annually.
Machine Learning Engineer
At an average annual wage of $131 001, this role pays higher, and that is due to the typical job requirements. As a machine learning engineer, your daily tasks will be creating data funnels and delivering software solutions. To succeed in this role, you must have proficient programming, static, and software engineering skills. Aside from the design and development of machine learning systems, you will also need to run tests and monitor the functionality and performance of these systems.
Machine Learning Scientist
Aside from the slight difference in average annual wage, there are other differences between a machine learning engineer and a machine learning scientist. This role requires that you research new algorithms and data approaches to be used in adaptive systems. These systems can be supervised or unsupervised. In some cases, they can also be deep learning techniques. Machine learning scientists usually like being called research scientists or research engineers. Machine learning scientists earn an average salary of $137,053.
Applications Architect
Tracking the behavior of applications a business uses to how these applications interact with each other and their users is one of the core job descriptions of an applications architect. Majorly, these professionals focus on the design of the entire architecture of applications. They also contribute to the building of user interfaces and infrastructure. As an applications architect, you could earn up to $129,000 annually.
Enterprise Architect
In every tech organization, someone should align the organization’s strategy and goals with the technology needed to execute these strategies. That is the job of the enterprise architect. Enterprise architects must clearly and fully understand the business, its strategy, and the technology needed. A system architecture can be designed through this understanding to meet these needs. The average annual wage for an enterprise architect is $150,782.
Data Architect
A data architect creates new database systems and discovers new ways to enhance the functionality of existing systems. Also, data architects ensure database analysts and administrators have access to these database systems. In addition, data architects also ensure design analytics applications are built for multiple platforms. In contrast, data solutions are built for performance. An average data architect earns $118,868 yearly.
Infrastructure Architect
Ensuring that all business systems work optimally and support the development of new technologies falls on the purview of the infrastructure architect. Similar to this job description is the cloud infrastructure architect, only that the cloud infrastructure architect manages an organization’s cloud computing strategy. An infrastructure architect could earn as much as $127,676 annually.
Data Engineer
The data engineer performs real-time analysis on stored and gathered data. Aside from batch processing of data, they also build and maintain data pipelines that create a robust, interconnected, and robust data ecosystem within a company. The responsibilities of the data engineer ensure information is available to data scientists. Data engineers earn an average annual wage of $112,493.
Business Intelligence (BI) Developer
Business intelligence developers are extremely data-savvy people who design and develop custom strategies to help business users conveniently find information needed to make better business decisions. These experts either use business intelligence tools or develop business intelligence analytic applications to help end users understand their systems. BI developers earn an average of $92,013 yearly.
Statistician
The collection, analysis, and interpretation of data to identify relationships and trends that could be instrumental in making decisions for the organization are done by experts. Statisticians are the experts employed to do these and design the process of collecting data and communicating the findings to the relevant stakeholders. Statisticians earn $88,989 annually.
Data Analyst
An average yearly salary of a data analyst is $69,517, and they are in charge of transforming and manipulating large data sets to fit the required analysis for organizations. For most companies, data analysts can also be asked to track web analytics and analyze A/B tests. Data analysts also prepare reports on insights and trends based on their analysis. These reports are presented to organizational leaders who use these reports to improve the decision-making process of the organization.
Conclusion
From dating apps to government security, data science experts are needed will continue to be in high demand. Millions of businesses and organizations depend on big data to better serve their customers. I guess we can say the same for every tech career. If you’re interested in going into tech, you should read this article on top tech careers in demand.
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