Want to become the Junior, Senior, or Principal Data Scientist at your company? Learn what you must do to reach the next level of the career game in the world of Data Science.
There’s three levels on which businesses hire Data Scientists: Junior, Senior, or Principal. Whether you’re only beginning your career in Data Science or interested in changing careers to Data Science, you will find that you’re at one of those levels.
The goal of this post is to shine some light on what’s required of each level of data scientist and what’s beyond the scope of each level. While companies may use different titles for these roles, this post gives the general base of expectations and levels for each role. Further, this post ends with hands-on advice on how to become ready for the transition into AI or how to obtain that well-deserved promotion.
The Data Science Skills Matrix
A Data Scientist is required to know three areas: Stats, Business, and Engineering. But, you don’t need to master each of the three areas from the start. What skills must you focus on while looking for an entry-level role? What skills are more valuable as you advance up the corporate ladder in data science?
The graph below displays the market expectations at each level. These results are from personal experience in this career field and talking to experts and influencers. To hopefully eliminate confusion, we shall refer to the various positions by their respective levels from 1.0 to 3.0.
Let’s review the demands placed on the three levels in further detail down below.
Junior — Level 1.0
The typical level 1.0 is a fresh, college graduate. Popular areas of study in college are Comp Sci, Math, or Engineering. This role has 0–2 years of experience and familiarity with building prototypes in Python by using structured data. This person has made a GitHub profile and participated in competitions at Kaggle.
Junior Data Scientists offer tremendous value to businesses. They are fresh from online courses in data science at Coursera or Udemy. They offer immediate help. They typically are self-taught because few colleges have programs in Data Science. They show tremendous commitment to furthering their knowledge and curiosity. The Junior Data Scientist is excellent at building prototypes of solutions, but this person still doesn’t have complete proficiency in business and engineering.
Junior Data Scientists need to have a burning passion for understanding algorithms in Machine Learning. You can show this passion by participating in open-source projects or kaggle competitions.
How They Work
If a business hires a Junior Data Scientists, there is typically a team of data science professionals already working there. The company is then intending to reduce the burden on its more seasoned colleagues. Doing so requires quickly trying new ideas, debugging the attempts, and refactoring already built models. You will talk about your ideas with other members of the team as partners sparring each other. You must pitch new ideas of ways to improve things. You must be responsible for your code, continuously trying to improve quality of your code and it’s impact. You’re an excellent team member, constantly seeking to aid your colleagues in their goal of creating great data products.
How They Don’t Work
Junior Data Scientists are not experienced at creating complex product or solutions. As a result, they works on teams to put models in production. Since they recently joined the firm, they are not immersed in the company’s business. They are still learning the ropes of the company’s industry. They are not required to make new products to impact the company’s Fundamental Business Equation. However, what is required at all times is the desire to improve your skills and learn more.
There’s a correlation between a data scientist’s ability to fully build our every part of a product and their ability to lead the team.
If you excel in your role as a Junior Data Scientist, then you must possess a strong understanding of Data Science models. You demonstrate insatiable curiosity for understanding the engineering and business of your firm to enhance your set of skills.
The Senior — Level 2.0
A Senior Data Scientist has experience from working as a Junior Data Scientist, Software Engineer, or finished a doctorate. He must have 3–5 years of experience related to the field, produces reusable code, and creates resilient pipelines for data in the cloud.
Good candidates for these roles have experience from past jobs in data science or related fields. They are also expected to have deeper knowledge of writing code and putting it into production.
Businesses prefer Senior Data Scientists since they offer tremendous value in exchange for a reasonable compensation. They have more experience than Junior Data Scientists, thus they don’t make costly greenhorn mistakes. Also, the company doesn’t pay them as much as Principal Data Scientists, but still requires them to create Data Science models that are production-ready.
How They Work
The Data Scientist Level 2.0 is an expert at placing models into production. Principal Data Scientists and Business Managers are responsible for assigning tasks, but Senior Data Scientist build well-designed data products. Senior Data Scientists know how to avoid logical flaws in the model, have doubts about systems that function “too well”, and prepare data correctly. Senior Data Scientist are tasked with mentoring Junior Data Scientists. They also are responsible for answering business questions from management.
How They Don’t Work
The Senior Data Scientist does not have the responsibility of leading a whole team. It is not the duty of the Senior Data Scientist to generate ideas for new data products since that is the responsibility of more experienced colleagues and managers. Senior Data Scientist must know the details for products which they designed, but they aren’t required to keep track of the overall architecture for every data-driven product at the company. The Level 2.0 Data Scientist has more skills with statistics and engineering than a Level 1.0 Data Scientist; however, they are not involved in the business aspect required of Level 3.0 Data Scientists.
The Level 2.0 Data Scientist is measured by the ability of their models to improve the business. This level of data scientist possesses a good intuition of how statistical models work and how to implement them. They are in the process of understanding how the company functions, but they aren’t expected to offer solutions to business problems yet.
The Principal — Level 3.0
The Principal Data Scientist has the most experience of any member on a Data Science team. They have more than 5 years of experience and are well-versed in multiple types of Machine Learning models. They understand the best practices for putting models into production. They write code that is computationally efficient and are seeking high-impact business projects.
In addition to possessing the best engineering skills and deeply understanding the functions of the models being implemented, they also firmly grasp the business function of their company. They have a track record of yielding business impact that improves the baseline through Data Science products.
How They Work
They are tasked with producing high-impact projects on the data science team. They closely coordinate with stakeholders to lead a possibly cross-functional group of professionals to yield the best answer to a business problem. Hence, their leadership skills are beyond Level 1.0 and 2.0. Level 3.0 works as a technical consultant to help the managers of products of other departments. With vast skills and experience from major Data Science categories, they are highly valued for any project.
They have seen why products fail, and therefore they successfully drive new projects. They are valued contributors to discussions on products and enjoy teaching the firm about Data Science. With experience at providing impactful Data Science solutions, they are valuable assets to the Data Science team.
How They Don’t Work
They shape the talk regarding desired skills, but they are not responsible for recruiting new members of the team. Although they understand their company’s business function and suggest new products that have high impact, it’s still the responsibility of Product Managers to pursue and obtain market adoption. They lead the data science teams, but they are not responsible for career progression decisions.