Data Scientist is one of the fanciest role in an organization. Iconic salary with Scientist suffix in the job role is making people crazy about it. People also call it ” sexist Job for 21 century“.Although Data Scientists have to perform the most challenging Technical tasks in an organization.That is the only reason for their higher pay perk. Companies like Facebook, Amazon and Google etc are hiring top college graduates for data scientist role with an amazing annual salary package. Still, There are so many hidden facts about Data Scientist salary which we will uncover in this article.So don’t miss if you are also interested in Data Science.
First, have a look at Data Scientist Salary –
Although you know salaries and wages are linked to companies overall performance.Data Scientist salary is no more an exception. For an Individual data scientist, Its a combination of market standard ( mean of other companies data science resources ) linked with companies overall performance and your very own individual performance.
Yes, it’s true. Like every other job profile, the data scientist salary also varies with experience level. For Example, You already know the data Scientist salary in the USA. Now let’s have look at senior data scientist salary –
The numbers for the salary is an average or median of the salary collected. Don’t you think what makes the difference between min and max salary on the same job profile? Yes ! you got it. This is what I am going to discuss throughout this article. There are some hidden factors which make differences. If you want to explore more about data scientist salary near your area. You may use the Glassdoor . It will give you an overview of salary.
How Organization shape Data Science Team –
An organization may have different job titles for Data Science Team members. The point is here that Data Science team need to work closely with Business side and Technical Team both.In that Situation, the best way to create organization hierarchy is to create a position for CAO ( Chief Analytical Officer ) which may incorporate with the Business head as well as Technical Head.
Now lets Further divide the team . Data Science project usually have two ends on the high level –
1.Data Science Mining Project and Team –
The scope of such project is to make sense out of the data.For example -If you need to identify the reason for the Stock market crash in the term of the available statistical market parameter. All you need to mine the values of these parameters at the time of previous market crashes.
Now let’s talk about Team structure, The manager or topmost employee of Data Science mining team should report to CAO directly. If you go below you can organize into multiple Team lead ( If so many projects are going with different teams), further under a team, there could be multiple senior Data Scientist, Data Scientist, Junior Data Scientist or Intern Data Scientist.
2. Predictive Analytics and Modeling | Machine Learning Project and Team –
Here you need to build some model using the historical data set available. For example, You may build some predictive model for stock price prediction.Team structure here may have similar like above.
Please keep in the mind, This is one of the suggested and common way to arrange the Data Science Team in an organization.It’s completely up to organization how they want to organize their data Science resources.As for as data scientist salary is concerned its depend on which role you are working. I mean as you seen in above graphs the senior data scientist get 21000$ on an average than data scientist. In the same way, salary decreases or increases.
Factors Affecting the Data Scientist Salary –
There are so many things which can create a big difference in salary even on same job profile. Most of the factors you already know which are common to experience level. I do not think you need any explanation on that. I will explain few of the below-written points. Anyways before we start exploring these factors for you. I will request you to read the section, the reason for salary variation for data science . Once you read it, you will come to know why people on the same experience level and same job level or drawing different salaries.
Now a quick notes for you on this section –
- Total Experience and Relevant Experience in Data Science
- Education Background and Certification for Data Scientist
- Data Science skill set (Technology, Mathematics, Statistics )
- Logical thinking and mental ability.
- Communication skills
- Domain Expertise.
Reason for Salary Variations in Data Science –
This section contains the hidden factor which people usually ignore about Data Scientist.Guess what! It is skill set.Actually, Data scientist covers three different field mathematics, statistics, and Programming. Everyone is itself big. In that situation, perfection in all of them is really rare. So usually people are good in mathematics and statistics but weak or average in programming. In the opposite side, some of the people have hands-on experience with programming and just average knowledge of statistics and mathematics. I mean there could be multiple combinations of weakness and strength in data science skill set.This different skill set creates a huge difference. Think from the employer perspective, If you can also write the code for very own predictive model. You are saving the salary for the developer. Obviously, you must get the rewards.
It was just the example. Let’s understand more professionally. Actually, Data scientist project starts with some PoC ( Proof of concept ) which need rapid development. In that scenario, you can not create the requirement specification for the developer. I hope you have a clear understanding of it.Let’s go a little deeper in the technical aspect. Suppose you need to create a data visualization view for some sort of analysis. If you know how to code it, Its good but if you know some data visualization tool like Qlik sense or any comparable. Obviously, you can save a lot of time and give more efficiency to your employer. This all gets converted into your salary.
As you know data science is too huge. Therefore, Here is always some scope for learning new. All depends on your learning ability and personal attitude. If you think you can combine all, See the world of opportunity is open for you. Go ahead and give a hit.
Education Background and Certification for Data Scientist-
Yes, it is really true knowledge worth more than just degrees but It is also a truth that If you have a relevant degree, you must have knowledge. To get a role in Data Science Team, You need the following degrees-
- [ Must have ] Bachelor Degree in Either Computer Science, Applied Mathematics, Statistics.
- [ Recommended] Master in any of the field or subfield like Computer Science, Applied Mathematics, Statistics.
- [Good To have ] PhDs in Data Science.
Apart from these traditional degrees, There is some academical certification program from Industry Leaders like Microsoft and Oracle etc. Here is a list of few goods to have certification for data scientist –
- Data Science Specialization by Coursera -Cousera needs no introduction. There is a variety of course and certification provide by Cousera which can accelerate your career as a data scientist. Here you can choose courses even if you are not Financially strong. There is Financial Aid facility available for economic students.
- Data Science Edx- Edx is also providing a range of relevant data science course which covers A-Z essential to becoming a data scientist. This course is linked to The University of California. Each course needs 10-15 per weeks and each week evolves 8-10 hours per week.
- CAP: Certified Analytics Professional
Data Scientist Skillset –
As I have already told, A data scientist needs to be strong on mathematics, statistics, and programming. In mathematics and Statistics, you need to have strong hands-on Probability and Linear Algebra. Specially the distribution like – Poisson Distribution, Binomial Distribution etc. Technical Requirement for the data scientist is really huge.
Technical Requirement ( Programming ) for Data Scientist –
In order to be a Data Scientist, you should know the following things –
- Expertise in Python, R or some comparable programming language.
- Strong hands-on experience on SQL and NoSQL database.
- Hands-on knowledge of Machine Learning Framework ( Scikit -Learn etc )and Deep Learning Framework ( TensorFlow etc).
- Knowledge of Big Data Technologies like ( Hadoop ).
- Hands-on expertise in any of the Data Visualization tool (Qlik Sense, Tableau etc).
Why Domain Expertise is too much important for getting Data Scientist Salary –
Every Data Science project starts with some challenge. If you are not aware of domain how will you figure the solution Right? Let’s understand it with some example. Suppose you want to build some a prediction model which can predict the inflation rate for a particular day. How will you choose the feature for your model? You just need the good level of understanding of Economics.
In the same way, the domain expertise is must to solve the complex problem in data science. Frankly speaking after doing two-three small level machine learning project, you will programming wise there is fix set of thing which you need to perform. The only challenge is here to choose the features and tuning the model parameters for highly accurate results.
Soft skills for a data scientist –
In order to have good numbers as a data scientist salary, you need to also focus on some of your soft skill set.
1.Good Communication skills –
It’s not just for data scientist but it is for every profession. Specially if you need to deal at the global level where your team members are sitting in different countries. Usually, what happens with MNCs, they have few development and research center but have multiple sells center. Now if you are the data scientist, you need to attend the sells call. Here you need strong communication skills. Right?
2. Good Team Player –
As a data scientist, you need to work cross-functionally.Sometimes you need Developers and some time with sells people. In that case, you need to be a strong team player to perform your task effectively.
3.Strong Decision Making –
Although as a Data Scientist, You are building a code which can auto take decisions. But to make those decision-making algorithms you need to have strong decision power. Right?
Similar Job Profiles as Data Scientist –
I have identified people asking the difference between data analyst and data scientist. Sometimes they also confuse with the role of data Engineer and Data Scientist. Truly speaking there are few similarities but we can not ignore the big gap as well. Here I going to list the task performed by the similar job profile as data scientist –
1.Data Analyst –
Data Analyst needs to fetch the data, clean them, organize them. Data Analyst needs to be good in statistics but machine learning and big data are not compulsory to them.This creates separate boundaries in Data Analyst and Data Scientist.
2. Data Engineer –
Every data engineer must be expert in technologies like Big data. The core responsibility for the data engineer to have the handling the large volume data. The skill set required by a data engineer is MapReduce, HBase etc.
3. Business Analyst –
To efficiently identify the requirement and scope of IT project with the market Gap.Business Analyst is responsible for this. Business Analyst must be able to access the database efficiently with the query language like ( SQL and all).
As a statistician, You need to design new statistics model and techniques. Max you can simulate them using some exiting tool. Nobody is going to say you to write the code.
Finally, Data Scientist is someone who can basics operation of Data Analyst, statistician and data engineer with little domain expertise. Again that is the reason for such high pay.
Other Traditional Profession Vs Data Scientist –
I will never recommend choosing any profession just because of money. Its a big decision of your life, It should completely be your interest. Just this comparison based on the current salaries. As per the article published in a Reputed Journal of India, ‘Times of India”.The article clearly states,”Data Scientists earning more than CAs and Engineers“. I again insist do not run behind money.I choose data science because I have very keen interest in it. I am happy if it is making me rich too.
Job Responsibility for a Data Scientist –
As I have already told you, Doing the job as a Data Scientist looks very fancy but It is really challenging. Data Scientist needs to perform these task –
- Transforming a real-life problem into a proper Data Science Project. I used the word proper data science project which means It should have some specific goals in.
- Gathering data from different sources whether from the structured data source like ( csv, sql database etc. ) or unstructured data source like ( Facebook or tweeter feed etc )
- Cleaning the data and create the usable dataset.
- Visualize the data for Analysis.
Frankly speaking, the above list of tasks are common for every data scientist. From now there could be a difference based on the project requirement. Like you have to mine some information or do some machine learning task. Here are few advance level task which data scientist need to perform –
1. Applying various data mining techniques like ( clustering and association)
2. Designing any machine learning model and tune it for better accuracy.
3. Sometimes you need to deploy your code or Integrate with some interface ( Web based or Voice based like chatbot and virtual assistant ).So knowing Cloud basics is good.
Are You Ready to be a Data Scientist?
If the future is data science what are you thinking and waiting for? Oh ! I got it. Do you need a learning path Right? No worries! Here is the complete path for you – How to become a data scientist step by step ? All you need to read and follow it. Here you will get information about the current market requirement for a data scientist. You will also get the relevant link to read from.
Biggest Challenge for a Data Scientist –
Artificial Intelligence is growing very fast. Data science, machine earning and NLP all such cutting-edge technologies which are interrelated. Every other day, you get something new. For a data scientist, it is the biggest challenge to keep working on current technology and simultaneously learning others.
If you need to stay longer in the data science market, You definitely have to face this challenges.No other option for you. Here you need to focus on basics because if you good basics you learn any tool and technology on the top of it.
My purpose behind this article is to make you more clear about data scientist salary and similar Job Profiles. Glassdoor ranked one to data scientist as a profession in 2016. Attractive salary and a respective career with data science need a lot of efforts. If you think this article adds some value for readers like you. Please share our content with them who also wants to explore data science.If you think that you may also add some information about data scientist salary and related facts. You may comment below we will contact you .