Technology August 15, 2020 Last updated August 14th, 2020 1,350 Reads share

Journey of Data Science Professional: From Level 1.0 to Level 3.0

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Steering your data science journey could be easy if you know the road map. Oh yes, there is definitely a road map unless you have been working in the related field at a senior level and suddenly decide to make a switch to one of the most lucrative professions even during the time of global crisis.

Even then it would be good to follow the steps to reach the pinnacle of success you have imagined for yourself as a Data Science professional.

But wait, what does a journey toward becoming a data science professional entail?  Let’s have a look at the roadmap before you embark on the road to becoming a data scientist.

Guide Map to Become a Data Scientist

Before I take you on this wonderful journey of fulfilling your dream to become a data scientist. Remember: Organizations hire data science professionals on three levels or on your skills and knowledge. The three levels include:

  1. Junior Data Scientist
  2. Senior Data Scientist
  3. Principal Data Scientist

Whether you are a fresh graduate or a seasoned professional eyeing the career of a data scientist, you would find yourself oscillating between these three levels. The job titles may differ with different organizations; however, the expectation of the role remains the same as per the roles.

Data Science: Skills Required

A data science professional is expected to proficient in three basic skills – Statistics, Engineering, and business. But take heart, you are not supposed to be proficient in all three right from the word go. These are the essential skills a data scientist must know, however, as an entry-level junior data scientist you are expected to be proficient in statistics, and then depending on your rising levels certain skills become important and others not so important. Let’s understand the skills levels of a Data Science professional through the table depicted below.

Data Science Skills Required

Levels /Skills Statistics Engineering Business
Junior: Level 1.0
Senior: Level 2.0


Principal: Level 3.0


If you look at the table carefully, a data science professional at an entry-level or at Level 1.0 is a Junior Data Scientist who is expected to be proficient only in Statistics. Similarly, for the other two roles Senior Data Scientist and Principal Data Scientist, the levels of skills increase as they climb the ladder of the levels of position.

You have to understand that the road to becoming a level 2.0 or level 3.0 Data Scientist i.e. a Senior Data Scientist or a Principal Data Scientist would go through these levels and there is no shortcut to it. Let’s examine the role of each level in a little detail now.

Junior Data Scientist Level1.0

Education Years of Experience Skills
MSc in Computer Science, Mathematics, Engineering 0-2 years Should be adept in Python, Jupyter, and Kaggle

Remember: A Junior Data Scientist could offer huge value to organizations. Here’s how – Since they have freshly completed online programs they could offer immediate help to already established team of data science professionals. Some of the qualities that go in their favor include –

  • Self-taught, as there are no degree programs in Data Science. So whatever they have learned is through their hard work.
  • It is their chosen field of work and thus they are committed to the profession and show tremendous curiosity.
  • They are eager to learn more; they are hungry for knowledge.

In simple words: As a junior data scientist, you are expected to be good at prototyping solutions though you would still be less proficient with your engineering skills and mindset of business.

Senior Data Scientist: Level 2.0

Education Years of Experience Skills
MSc. Or a Ph.D. in Computer Science, Mathematics, and Engineering 3-5 years Docker, Cloud, APIs


Moving up to another level that is Level 2.0 is your Senior Data Scientist. Why do organizations hire or need a Senior Data Scientist? Simply because a senior data scientist brings to the table slightly advanced skills and knowledge at a reasonable salary. Another reason could be that they are reasonably less expensive as compared to a Principal Data Scientist and little more experienced as compared to a Junior Data Scientist. While they have not yet reached the proficiency levels of a Principal Data Scientist, they are still able to deliver Data Science models in production.

Note: The role of a Senior Data Scientist could be a fun play as you have completed Level 1.0 and there is still an opportunity to reach Level 3.0.

Principal Data Scientist: Level 3.0

Education Years of Experience Skills
MSc. Or a Ph.D. in Computer Science, Mathematics, and Engineering 5+ years Leadership, Business Acumen

Is this where the game ends for a data scientist? You would think yes and in a way yes, post this there is no room to build upon your technical skills but this is the position where you need to develop other skills like leadership qualities and sharpen your business acumen. A principal data scientist, in addition, to flawless engineering skills and a thorough understanding of the data science models used, should also be able to grasp the business of the organization they work for. As a principal data scientist, you would be expected to have a track record that shows how your knowledge and skills have impacted the business baseline with your impeccable data science skills.

So what are you waiting for? Gear up and get ready for your journey and yes, don’t forget to boost your skills and get your knowledge validated by credible certification bodies like Data Science Council of America (DASCA) or Dell EMC among others.

DepositPhotos – data

Niti Sharma

Niti Sharma

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