Data science specialists have risen in the job market. This is due to the fact that data science programs have been used by several upcoming business organizations. This has lead to an increase in workers for these organizations.

The high salaries are due to the fact that organizations have noted that data scientist vs. data analyst are great assets to the organizations as they are used to make smart decisions for the organization depending on the data they have collected and analyzed.

According to several studies, they have shown that the average salary of a data scientist is about is $113,436. Data scientists bring lots of cash and value to the organization. Sometimes it is a difficult task to get the right candidate who has the experience and qualification to handle all the tasks of a data scientist. This has been brought about by the competition in hiring them, thus becoming a challenging and fierce task. Due to this case, businessmen and women are willing even to pay higher salaries, especially to those qualified and experienced data scientists. In this article will look at data scientist’s salary.

The other reason for the high salaries is that data scientists are still on demand, and the starting salaries at entry-level are still high, especially those who have master’s in data science. In recent times data scientists’ salaries have become closer and almost the same as that of software developers; the difference is just small between the two despite the fact that they have the same education level.

There are several factors that determine the salary. They include:

Experience

Research has shown that experience is one of the key factors that determine the salary of a data scientist; thus, the more the years of experience, the higher the salary. Experience the most important factor that determines the salary scale for the data scientist.

There might be several applications at a given time, but the skills might be limited. Sometimes the businessmen and women may talk about the shortage of data scientists, but in this case, they refer to the lack of right candidate or data scientist with the minimum experience.

The entry levels and internship are several, but the organization would like to employ only experienced ones, thus translating to a high salary bracket. Sometimes it’s very difficult to get a graduate in data science to get a job given the fact that there is high competition in the job market.

As we have seen, data scientist professionals are one of the best jobs in the United States.

• For a new entry-level the starting salary is still high, and in most cases, the average is salary is $95,000;
• At the mid-level, it’s about $128,000, while those that have added responsibly, such as managers, can take home $185,000;
• For the experienced ones according to research, the average salary is $165,000 while those in managerial positions the average salary is $250,000.

Size of organization

The size of the firm also determines the salary of a data scientist, thus the bigger the firm, the higher the salary.

For instance, if the company has many employees, the salary is likely to be higher than in that firm that has few employees. Companies that deal with technology have their salaries earning huge salaries. The higher the profile an organization is, the higher the salary data scientists earn.

The region

The salary of a data scientist also depends on the regions they are. According to research, California has the highest-paid data scientists. While other parts pay slightly lower salaries as the cost of living may be lower. Therefore the high the cost of living in the region is the higher the salary the data scientists are paid and vice versa.

Education level

As indicated earlier data science profession is growing rapidly while experience and skilled scientists are very low and scarce. Therefore the competition is high, especially in large firms such as manufacturing technology, healthcare, financial and among others. Several firms are looking for qualified, experienced and skilled data scientists in order to maximize his skills as they bring more money to the firms as they do the research and analyze data. This helps in making proper and s scientifically proven decisions for the firm.

In most cases, they have master’s degrees and Ph.D. They also do some on training tasks, especially to sharpen their skills on how to use their computing devices. It’s recommended that one can enroll for advanced education in a related field in order to sharpen the skill learnt at the undergraduate level. Consequently, as the skills raise the same as will be in the salary scale, this will help one to do more complicated research for the firms that will help them to grow.

Since they are rare, especially those who have a combination of both skill and education. Data scientists must have the basics of coding. Several data scientists must be able to code always if he or she wants to be among the highest-paid while those who don’t code have the lowest salary.

Companies require data scientists that have skills, knowledge on modelling so as the can derive complex data and analysis, thus may include skills on digital media, databases, social media, machines, data, and documents, and web.

Data scientists who have advanced degrees in other disciplines like statistics, applied mathematics, operations research, engineering, economics, computer science or. Data science, give some an upper hand to get a high salary.

Soft skills

In addition, data scientists also require soft skills, good communication skills, leadership qualities, especially in cases of managerial position business acumen scientific knowledge and curiosity. This will help to earn him or her high salary compared to those who don’t have such traits

Conclusion

Data scientists play a vital role in data analysis to give valuable decisions and insights. The companies need to know before investing their hard-earned money in technology and other departments. By providing such information, they earn more cash as part of their services.

As they are experienced, they advise the firm on where to take risks as well as SWOT analysis. In so doing, they are able to ask the relevant questions in regard to the data they have since he spends most of their time analyzing and preparing these data.