The data science career path from junior to senior data scientist varies greatly in skill level, responsibilities, daily tasks, and everyone’s favorite topic– total compensation.

For any kind of technical role, including data science, there are two paths: the management path and the individual contributor path.

The individual contributor path includes data scientists who work on core projects, contribute code, run analyses, and build ETL pipelines and machine learning models. The management path encompasses data scientists who manage people, scale data strategy, and work on fitting the pieces of a data organization together.

Both paths originate from the same journey from entry-level to the senior data scientist position, where they then diverge. As career advancement continues, individual contributors can decide to become managers or remain highly specialized data scientists.

For this article, we're going to focus on the individual contributor data science career path, and what it takes to level up as a data scientist.

Data Science Intern & Entry-Level Data Scientist Role

Junior data scientists and data science interns typically have work assigned to them with lower pay.
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Let’s start with junior data scientists and data science interns. What do you have to know and what do you actually do?

Honestly, not that much. At this stage, data scientists are super raw and mainly work on developing their core technical skills, like SQL and Python. As such, tasks are generally straightforward and have a clear objective goal.

If you’re a data science intern, you can usually contribute value by building scripts or prototyping projects with data visualizations and models. You won’t have too much time to ship production code or build up understanding of everything in the business, so the best thing you can do is add value wherever and whenever possible.

Junior data scientists also work within a specific scope. For example, instead of tackling ambiguous analytics problems (e.g. what’s influencing customer purchasing behavior on our e-commerce website?), an entry-level data scientist would more likely be given a task to write a query to calculate customer churn rates or build a dashboard to look at purchases by marketing channel.

If an entry-level data scientist were to be given a more advanced task of building a model and deploying it into production, there would probably be a daily check-in with senior data scientists to help them get unblocked, do code reviews, and learn how to integrate into the existing system.

Notice the keyword I’ve been using here: junior data scientists are given a task, instead of finding one. Understanding what to build separates junior data scientists from more advanced roles. A junior data scientist might brainstorm on strategy and architecture, but the majority of their tasks are focused on producing work for their managers and other stakeholders.

Salaries for data science interns can usually range from minimum wage to $40 per hour at some FAANG companies. Similarly, entry-level data scientists are usually paid around $80k-$100k per year, but it’s pretty common for entry-level data scientists at FAANG companies to make over $150K a year in total compensation, even in their first year. In this case, these companies are hiring for the data science potential more than the actual value these scientists provide.

Looking for FAANG interview guides? Check out our articles on Apple, Netflix, and Google data scientist interviews.

Mid-Level Data Scientist

Mid-level data scientists have more experience and are able to schedule their own responsibilities more, earning higher salaries.
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After around one to two years of experience as an entry-level data scientist, you can transition into a mid-level data scientist role. Mid-level data scientists are advanced individual contributors that can take up larger project scopes and more ambiguous business problems.

For example, while a junior data scientist would be creating the SQL queries for an ETL pipeline, a mid-level data scientist should be able to architect the entire ETL pipeline from scratch and use it in their own machine learning model.

A data scientist who has moved past the junior stage won't need as many check-ins and can usually unblock themselves without having to ask other data scientists for help. Additionally, from a product perspective, a mid-level data scientist has a higher level of understanding of business problems and how to use data science to solve those problems.

This advancement in skill and experience means more autonomy in terms of project choice and project management. Data scientists will always have more than enough projects to work on. Prioritizing projects is the first step in leveling your career so that someone doesn’t have to manually assign work to you.

Looking for a role that bridges data science projects with data engineering? Check out our article on the data science engineer role to learn more.

Most mid-level data scientists can earn anywhere from $120k-$180k in the Bay Area and other cities, no matter where they end up working at. At FAANG companies, this number can shoot up to over $200k a year in total compensation.

Senior Data Scientist Career Path

Senior data scientists have more responsibilities and higher salaries compared to other data scientists.
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Finally, what separates senior data scientists from the rest? While years of experience still matter, there are better features that differentiate a more senior data scientist from a less senior data scientist.

Once someone has worked in data science for five to seven years, their title will likely be denoted as senior. But someone with 20+ years of experience could still be a worse data scientist than someone with five years of experience. Many companies have now instituted different “levels” to evaluate candidates that are going for individual contributor roles that help determine total compensation as well.

Looking for a career that bridges the gap between product management and data science? Check out our article on the data science product manager today!

The skills of a data scientist can be measured in interviews by testing speed and accuracy on technical problems, but also by evaluating communication skills. Then, on the job, many of these skills are more observable. For example, senior data scientists should be able to:

  • Onboard themselves on business and technical architecture
  • Have high data accuracy and quality
  • Good code quality and completeness
  • Understood project scope and where to prioritize applications of data science
  • Good communication of technical concepts
  • A strong ability to mentor junior data scientists

Scoring well on these different traits determines how senior a data scientist will be. The best data scientists can take a highly ambiguous problem and architect a solution from beginning to end by themselves or in a team environment. The level of efficiency with which they can complete this task determines their value.

For example, let’s say a startup wants to build its first A/B testing system. A good senior data scientist would figure out business requirements and scope–

  • Why do we need an A/B testing system?
  • Do we need it for email, the backend, or only the front-end?
  • How many users does the system need to handle in the future?

Now that the scope is laid out, the senior data scientist would begin architecting a system to build. They would think about how to randomly distribute the users into different buckets, how to create different functions that other data scientists can reuse in their code later on, and what kind of deliverable would be built so that product managers and executives could run experiments and monitor tests.

So how much do they get paid? The short answer is a lot. A senior data scientist, depending on their level, can make anywhere from $150k to a million dollars per year or more. The best senior data scientists understand how they can justify their salaries.

Ultimately, at the end of the day, data science career progression can be like any other role. As human beings, we take on bigger and bigger tasks as we gain experience in the game of life. Your value as a data scientist then corresponds to how much value you can add and the solutions you build relating to data.


Thanks for reading! Check out our recent articles on how we classified interview questions on Interview Query and hiring a data science intern.