Data Scientist Job Salaries Analysis

Data scientists are in high demand in many industries and sectors. But how much do they earn and where do they work?

Data Scientist Job Salaries Analysis
Photo by Tima Miroshnichenko


Data Science and Machine Learning are increasingly gaining popularity in various fields such as Sports, Art, Space, medicine, healthcare, and many more. It would be insightful to have a look at the salaries and current employment status of these data scientists across various places around the world.

The dataset was downloaded from Kaggle (link given below) and we will be making an exploratory analysis of the data, and visualize it.

The dataset is divided based on Experience level as follows:

  1. EN: Entry Level
  2. MI: Mid Level
  3. SE: Senior Level
  4. EX: Executive Level

The dataset is divided based on Employment types as follows:

  1. FT: Full Time
  2. PT: Part Time
  3. CT: Contract basis
  4. FL: Freelancer

The dataset is divided based on Company size as follows:

  1. S: Small
  2. M: Medium
  3. L: Large


Exploratory Analysis and Visualization


In this section, we will be doing exploratory data analysis and visualization of a given dataset. The following items are on the agenda:

  1. Distribution of Experience level
  2. Distribution of Work type
  3. Comparing salaries of Data scientist jobs based on Experience Level
  4. Comparing salaries of Data scientist jobs based on Employment type
  5. Comparing salaries based on experience level and Size of Company
  6. Comparing Data scientist salaries across the world
  7. Average salary as a function of currency
  8. Average Salary as a Function of location
  9. Top 10 Data Science job positions
  10. Remote work status as a function of time



In the dataset, 14.5% of members are freshers while most quota is filled by Senior Engineers at 46.1%.



During the period 2020–2022, 62.8% of members have shifted to work-from-home modalities owing to Covid — 19 crisis. Later, we will see the trend shifting back to normalcy.



Naturally, it can be seen that the more the experience, the better you get paid for it. However, at the highest executive level, the salaries vary much more as compared to other levels.



It seems contract-based jobs earn the most out of all types. Although the variation in their pay scale is also too high. An interesting observation is that freelancers earn more than part-timers but variation in their pay scales almost looks proportional.



Adding a hue ‘company size’ for the previous ‘experience level’ vs ‘salary’ graph reveals more information. Senior levels job salaries on average coincide with Executive level salaries. Moreover, on average senior levels jobs salaries of small companies almost coincide with executive-level salaries of respective company size.



By summing the salaries column we end up with very skewed data towards the USA. This might be because of various factors like most data scientist jobs being created in the USA, the data is mostly collected in the US, or the data collection form might be in English, and this form might have been circulated in non-English speaking countries. However, in order to linearize the data, we will take a log10 scale on the salaries column and these scaled values are passed for drawing the colors of the map.



The sum of salaries might not be a correct measure for comparison as the entries in a particular country might be more than others. So we plot the mean keeping the log10 scale. This gives a much better idea of salaries across the world.



It can be observed that the majority of data science jobs are in the United States of America (US) and it also has the highest-paying jobs. Canada (CA), Japan (JP), Germany (DE), United Kingdom (GB), Spain (ES), France (FR), Greece (GR), and India (IN) follow in terms of highest job salaries and a number of jobs (except Japan) in that order respectively.



Taking average salaries as a function of currency reveals that people earn most in USD, followed by the Swiss franc and Singapore dollar. This graph is heavily influenced by the value of a particular currency as most currencies on the left-hand side of the graph have relatively high values against USD.



The location of the company also plays a vital role in determining the mean salaries. The top 10 countries in terms of average salaries are plotted.



It can be observed that Data Scientist is the most common job title followed by Data Engineer and Data Analyst respectively.



Due to the Covid — 19 crisis, most jobs were shifted to Work from home modality, however as the vaccines started rolling out, everything starts returning to normalcy.


Inferences and Conclusion


Detailed data analysis is done for the given dataset of Data Science Job Salaries. It can be concluded that:

  1. Data Science is one of the most popular and emerging fields in almost all industries such as Healthcare, Sports, Art, etc.
  2. The variation in the average salary of data scientists across the world is explored.
  3. The variation of salaries across types of employment such as Contract basis, Full-time, etc. is very crucial.
  4. The variation of salaries as you gain experience is a rising curve.
  5. Owing to the Covid — 19 crisis, the work environment was shifted to Work from Home and back to normalcy as time passed.


References and Future Work


All the useful links are listed below:


Nikhil Purao is currently pursuing a Masters of Technology degree from IIT Guwahati with a focus on data and decision sciences. As an AI enthusiast. He is passionate about using advanced analytics and artificial intelligence to drive business growth and improve outcomes. Through his studies, he have gained a deep understanding of the latest tools and techniques in the field, and he is committed to staying at the forefront of this exciting discipline. Whether it's uncovering key insights from complex datasets or developing cutting-edge solutions, he is always eager to take on new challenges and collaborate with others to achieve success.

Original. Reposted with permission.