Why Data Scientists are getting high paychecks ?

Let’s create a scenario where you are sitting on a sofa watching TV, sipping juice and eating snacks and a question pops in the room targeted at you- “What do you want to do in future?; What do you want to become or achieve in life?” you answer hastily and dodge the question maybe giving an apt reply maybe not but the keywords remain the same for everyone- Engineer, Doctor, Astronaut, Artist, Dancer, Singer etc. But this scenario has changed since the last decade as a new, challenging, interesting, exciting and highly paid cousin of the above careers has been born-“Data Scientist”. “A Data Scientist is one who is better at statistics than a software Engineer and better at software engineering than any statistician”. It’s a creative and needing amalgam of Statistics, Mathematics and Computer Science engineering and that’s why data scientists are getting high paychecks.

One could ask a question –“When data science was not an academic study and data scientists were not in the market was there no need of them as data has been there from the beginning?” well need lays necessity and necessity gives idea or in this case opportunity. Answering the question, they have been from the beginning working and solving problems through the respective domains of IT engineers, Mathematicians and Statisticians but not under the name tag ‘Data Scientist’. Data Scientists do get paid so much, but they make it worth with their skills and knowledge. Data science is a clever combination of Statistics, Mathematics and Computer Science engineering and with knowledge of the business employed in becomes a Data Scientist. In layman’s words Data Science is the discipline of making data useful and Data Scientist understand, categories, refine and use the data in the betterment of the business or their enterprise.

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Data is piling on with each minute of the day passing with 2.5 quintillion bytes of data created each day and it is only increasing exponentially. Studies and reports have shown that Ninety percent of the world’s data was created in the last two years: Report by IBM Marketing Cloud cited in an article in 2017. Eighty percent of all corporations data is nondescript, simply put unorganized. This data is unruly, unstructured and chaotic resulting in no actual use of it. To organise this data create Data Visualization to clearly understand the trends which could affect the business growth positively comes in Data Scientist. With the skills of an IT engineer, Statistician and most importantly knowledge of the particular business he/she is working for accounts for the high pay.

 Data Scientists is the highest paying job! Yes, we know that CEOs stand on top with an average salary of $740,589. But among the remaining jobs for the rest of the 99.999% working population, data scientist’s salaries are 113% more than the median salaries for other job postings, cited by job sites like Indeed.com. According to sites like Glassdoor and Indeed, the ongoing average salary is $118,709 and $123,000 respectively. But this varies extensively rooted around several factors like your actual title in the enterprise, responsibilities and pertinent job skills. Data Scientist is a sapling in the start and with each skill he/she acquires adds another stem and helps them turn into a lush green full-grown providing shadow and fruits to the business.

In this analogy, each stem pertains to skill acquired and polished with time like programming, statistics, linear algebra and calculus, software engineering, communication, machine learning etc. And, the fruits are Data Visualization, Data Wrangling and organisation, which is easy to understand and helps in the growth of the business. These skills and responsibilities of Data Scientists demand for high pay and has resulted in an average salary increase of 6.4% from 2016-2017.Data is money. Hard role to fill and three different main areas of prowess required more demand and less availability of Data Scientists are the reasons and justified grounds for a high salary base. And answers the question aptly, as to why data-scientists are getting high paychecks?

Importance of Domain Knowledge for Data Scientists

Domain knowledge i.e. knowledge of the particular zone of business they work in for example medical data, ecological data, banking or retail data in respective enterprises is one of the important aspect and skill to be or become a good Data Scientist and do work amounting to something, as stated by Mitchell A. Sanders. Understanding the data of the business is in itself special area of expertise which only comes with working in the same domain or the same comes with studying the business itself and asking a lot of question while working there, which takes time. This is the reason that everyone will agree to the assertion, “Enterprises and businesses should focus within and consider for a Data Scientist” of Svetlana Sicular, Gartner analyst.

She also adds, “Businesses and corporations already have employees who are familiar with their own data than any mystical data-scientist.” Or, as Svetlana Sicular notes that,” Learning Hadoop (another important software knowledge required of a good data-scientist) is easier than learning the company’s business.”

Four reasons why data scientists are getting high paycheck ?

It’s difficult to know all the ins and outs as to why the salary of data-scientists is so high? Or why being a data-scientist is so rewarding or why data-scientist job lands on the top level of the pay scale pyramid, but the following reasons will definitely clear all the ifs and buts and whys and how’s regarding this heading.

1. Advanced Core Skills are needed

We have established that it is not everyone’s cup of tea. But it is also not impossible to achieve or learn data science and become data scientist. The demanding and challenging nature of the position is the reason for their high pay. And, more than the median salary package of the other jobs. One must be full versed in the fields of Mathematics, Statistics and Programming just to start and this does not make one a full stacked data-scientist all the good companies are looking for. Then enter the plethora of skills which are acquired with time and experience to deal with day to day data and produce valuable information. Good knowledge and working capabilities on programming languages and software like Hadoop, C++, Java, Python, Hive, Pig and SQL are core essentials. Moving data in itself is another speciality and required skill reserved for ETL (Extract, Transformation and Loading) expert. Informatica, Teradata, MS SSIS bulk loading tools are just some tools among the range of ELT tools.


Knowledge and expertise of the business domain, programming and database skills, data modelling, unstructured data skills, statistical and mathematical tool skills to analyse using SAS, R, Excel or other tools, storytelling. While the visualization tool skills like Flare, AmCharts, D3.js, HighCharts, Tableau, Google Visualization API, Raphael.js, MS Paint and Substantive expertise in not all but some are required to get the job done.Lack of universities offering data science courses poses a problem. Kaggle (Data Scientist community) acquired by Google Cloud in 2017, surveyed the state of data science and machine learning reported in 2017 that, a big chunk of the full- time practitioners learned data science through unconventional tracks. It was reported that Thirty two percent of the surveyed data-scientists learned through MOOCs (Massive Open Online Courses), and Twenty seven percent earned the skill on their own- both of which are demanding and can only be achieved by high motivation, an unconventional attribute.

2. Impact on Business

Past data, present data and future data was, is and will be stored in a very clunky format. To understand it, access it and make sense of it Data Scientist are hired. Importance of data is unparalleled as in it helps in all the decision making executive members of the enterprise to make day-to-day resolutions which are going to decide the future of the same and help in making sound decisions for the company. Right data- scientist with good knowledge can wave the wand and provide enterprise with the refined, distilled and important information they were in dire need of. Though it is not the case with all, but will probably help in some other case.

Data Scientists of today are required to excavate all internal and external data sources and present findings to their stake holders. This presentation of data is another required skill of them which come under the category of Data Visualization and storytelling skills just piling on the never ending relay of expected abilities out of data-scientists. This handling of data and making it useful is easier said than done and hence justifies high pay and demand of Data Scientists.

3. Demand and Supply

A conventional market property familiar to economists is the understanding of demand and supply. A simple concept but true in every nature and in the case of data-scientists also. As supply decreases cost goes up and if demand increases then also cost goes up. But in case of data-scientist demand is more and supply (or availability of qualified and skilled data scientists) is less. Creates a merge effect and hence account as the primary for the reason as to why the data-scientists are paid so much. As data accumulation increases day by day and with increase in demand and need of data-scientists to analyse, organise and make the same data useful, companies today are in dire need of the one to better understand the data, but these qualified candidates are hard to find in the marketData Scientist Techcoffes.

Crowdflower (a data scientists platform) released a report in 2016 which cited that Eight three percent of the respondents felt that breed of data scientists is lacking in supply and is scarce which itself is a rise from 2015’s Seventy-five percent. The platform pursues in offering its readers better comprehension of data scientist market and talent.

4. Industry Knowledge

Knowledge of industry, business or the domain is additionally required to put the blend of well-versed maths, statistics and programming skills to well use of analysing the heaps of raw data. Different industries and sectors like banking, retail, insurance and automobile all require their data scientists to poses respective knowledge of their domains, as it plays a vital role in to point data analysis. That is why it is suggested by many as to look for data scientist within their own company with the required skill set to analyse data as the person will be having the knowledge and experience of the respective domain than a new out sourced data scientist as it is suggested by different senior data scientist. Companies should heed to this advice as ultimately they will be making decisions for the future of their company based on the analyses of the reports of these employees only for the betterment of the same.

There is a problem in many organisations as they tend to require of their data scientist to solve spot on problem, which are generally recruited from a whole different type of industry unaware of the finer variations of the data or information they are given access to. This only result in less organised data reports, expectations of the report and data analyses not being met. I am further becoming one of the reasons why young data scientists are leaving their jobs at an early stage. And a drop of water leaks from the already empty glass.


One can now understand that why data scientists are getting any high paychecks  and appreciate the value of a data scientist in general and as a part of an industry. And know the importance of data scientist in handling and analysing company’s data. That concludes the reasons, explanations of the reasons and examples which justify as to why data scientists are paid so much and clearly we can see that the salary is well deserved. Their blend of well versed knowledge of different and difficult fields like mathematics, statistics and programming along with a relay of skills and domain knowledge lends them in the category of high paying jobs. It’s not unfeasible or unimaginable or impossible to become a data scientist. It’s just that it requires a good amount of learning to evolve and sharpen all the skills required and listed above, with a little bit of persistence and hard work one can achieve anything.

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