As the quantity of data created by us is increasing day by day so the opportunities for the Data Science jobs. With thousands of opportunities available in the field and the different roles existing in the field, it has become necessary to understand the minute differences between each role. There are a number of candidates who apply for Data Science positions but with a dearth of skills, they never get selected for the desired position. Among many loopholes one lies is -"lack of information about the desired role". In many of the cases, aspirants do not know about the responsibilities each position carries. Because the positions are many in the field so there are differences in the skills each position requires. I have discussed two such roles here-"Statisticians" and "Data Engineer" which are confused a lot by the aspirants.
While there are always some roles and responsibilities that are particular to a company and I will suggest you analyze your company first on that part. Apart from that this article will give you a clearer look into the job comparison between "Statisticians" and "Data Engineer" role which after reading this article will remain deeper in your understanding.
A Data Engineer is the one who is the first one to handle your data. Whether it is cleaning your raw data, managing it or warehousing of the data to assemble it at one place for further use, all the responsibilities are handled diligently by Data Engineer.
You should never mistake a Data Engineer role with that of analyst because candidates often confuse that Data Engineer works on analyzing data and finding insights as well, which is not the case here. A Data Engineer is well fit to do all the things required by the organization before analyzing the data. Without him, it would be difficult to manage all the data and perform operations on it without cleaning of raw data.
A Data Engineer does not require many skills as in the case of Data Scientists and Machine learners. Proficiency in Database solution languages especially SQL, Programming Languages like Python, R, C++, Hadoop is some of the skills that a Data Engineer should have.
A major part of your work will be in dealing with numbers and statistics. Statisticians are the key employees in the organizations helping the organization to interpret complex statistical models which are hard to interpret by any analyst. Your work will be involved in finding insights and relevant information out of complex data involving statistics and numbers. Obviously, the role will require a lot from you on your quantitative part, so work more on that.
Apart from having a strong command in statistics and mathematics Statisticians should and must have strong communication skills as the information that you will gain after analyzing complex statistics models will have to be communicated to the different departments of your organization and without a flair in communication, all of your findings will seem a waste. Because you will provide findings and then the organization will take the necessary decisions and actions, lapse on your part in delivering clear findings will serve no good to the organization.
These were the job profile discussion of a Statisticians and a Data Engineer. I hope you must have developed a clearer idea of these two profiles now. The differences between the job roles of a Data Analyst and a Data Scientist have already been discussed in my previous articles which you can easily find on the platform.
I will Come up with more such clear differences between various profiles in Data Science. Because Data Science is an emerging career and you must have the understanding of each profile in that, a right approach will help you chose the best position among various in that.