The Data Science Career Path and Skills Progression (2024 Update)

Most people start their data science careers as interns or junior data scientists. However, you can progress quickly along this path and earn the rank of senior data scientist in just a few years. What is this process like, and what does it take to achieve this?

Although years of experience can make a difference, many companies, especially tech companies, are more interested in your skills and ability to contribute to their goals. This is what accelerates many data scientists’ careers.

In this article, we look at the data science career path from intern to senior data scientist. We’ll highlight the typical responsibilities at each level, salary ranges, how your relationship with others in the organization evolves, and the skills expected of a data scientist at each stage.

The Role

Interns and entry-level data scientists are often assigned relatively straightforward tasks with clear objectives. Your skills are still developing at this stage of the data science career path, and you probably lack the experience and insight to take on more abstract problems. However, this is also an excellent opportunity to improve core technical skills and learn more about the business. You may be asked to participate in brainstorming sessions and other activities, but most of your responsibilities will be focused on delivering on your assigned tasks. Being a team player is essential so you can be included in more projects and gradually assigned more responsibilities.

A typical task assigned at this level would be along the lines of writing a query to calculate customer churn rates or creating a dashboard that shows purchases coming in from different marketing channels. These tasks have a limited scope, but they’re still important. When given more advanced tasks, you will have to regularly check in with a senior data scientist.

Familiarity with SQL, Python/R, and visualization tools such as Tableau is a common requirement for entry-level data scientists and interns. These tools enable them to handle typical tasks assigned, such as writing simple queries, automating simple tasks, and creating dashboards.

Entry-level data scientists in industries such as tech and consultancy are often fresh graduates. However, similar positions in finance may require a few years of experience. Data scientists in other fields, e.g., healthcare, may also need an educational background or experience in the same field.

Salary

At some companies, a data science intern may earn anywhere from minimum wage to $10,000 a month. Entry-level data scientists usually earn $80,000 to $100,000; however, others earn over $160,000 at some FAANG companies. In such cases, the company is likely banking on the future potential of such hires, not the value they can deliver in the present.

Check out this page on Interview Query to see how entry-level data scientist salaries compare to entry-level salaries for other positions.e.

The Role

A lot of data scientists transition to mid-level positions after a year or two at the entry-level. This is a key stage in the data science career path because you’ll handle more ambiguous problems with wider scopes. Data scientists at this stage have more autonomy over how to handle projects. Depending on the industry, they may still get the overall project direction and big ideas from the seniors. Still, they will usually be entrusted to handle most of the technical work independently.

For example, a mid-level data scientist could be asked to construct a machine learning model and the entire ETL pipeline that will provide training data. This means designing and building the ML model, plus writing the code to collect, clean, and analyze the data to train it. This will require writing advanced SQL queries and complex programs in Python/R. Familiarity with ML tools and libraries such as TensorFlow and PyTorch will also be important.

Mid-level data scientists need soft skills like problem-solving and the ability to work in a collaborative environment. They also need to be able to communicate effectively with both technical and non-technical team members to help ensure accurate and relevant insights. They may even start giving input on other projects they’re not directly working on.

Additional qualities expected of a mid-level data scientist include:

• A good understanding of business problems and know how to use data science to solve them
• Ability to prioritize the right projects without needing someone to assign them
• Delivering results while needing less regular check-ins
• Ability to troubleshoot most problems without the assistance of another data scientist

Salary

Based on the most recent data, the average salary for mid-level data scientists is $133,827. However, some data scientists at this level have a base salary of over $200,000, and a select few have a total compensation of over $400,000.

Visit this page to see how mid-level data scientist salaries compare among the top companies.

Senior data scientists can take a highly ambiguous business problem and develop a model for solving it from start to finish. They are also expected to do this with a high level of efficiency. In many industries, they lead data science projects and are free to decide on the what and the how with minimal input from management.

For example, if a startup wants an A/B testing system, a senior data scientist will determine the business requirements and the project’s scope. They would then come up with the architecture of the system to be built and figure out how to distribute users, how to create reusable functions, the deliverables that should be built so product managers can run and monitor tests, etc. Hard skills are required, as well as high-level expertise in statistical analysis, machine learning, predictive modeling, programming, data warehousing, etc. It may take less than 5 years to reach this level in the tech industry, but it could also take 6+ years in industries such as finance.

Soft skills are critical at this level. Senior data scientists must be able to properly articulate insights to senior management. They need leadership skills to manage their teams and strong business acumen to ensure their projects align with the broader company goals. In some firms, senior data scientists also participate in the overall decision-making and must be strategic thinkers who can influence business decisions and determine organizational culture.

Qualities that will help you to excel as a senior data scientist include:

• Data accuracy and quality
• Ability to produce high-quality code with an excellent level of completeness
• A good understanding of project scope and which data science problems should be prioritized
• Ability to onboard yourself on business and technical architecture
• Skills in communicating technical concepts
• Capability in mentoring junior data scientists

In companies with many senior data scientists, these qualities also serve as evaluation criteria for determining the actual seniority level of senior data scientists and their total compensation.

Beyond Senior Level Positions

Becoming a senior data scientist isn’t the end of the data science career path. Although you can remain on an individual contributor path, you can also transition to managerial roles and start leading cross-functional teams. Many companies require at least five years of experience to qualify for these positions to ensure you have enough work experience and exposure to different teams.

Salary

Based on the most recent data, the average base salary for senior data scientists is $149,530, with the average total compensation exceeding $200,000. Some base salaries may be as low as $109,000, but other senior data scientists have base salaries of over $200,000 and total compensation packages exceeding $500,000.

For those who transition into managerial and executive roles, the total compensation can be much higher. You can find out more about senior data scientists’ salaries on Interview Query.

The skills of a data scientist can be measured during data science 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.

1. Do I Need a Degree or Other Academic Qualifications to Advance in Data Science?

The most important thing in data science is your ability to harness the power of data and help organizations meet their business objectives. Therefore, you don’t need a degree to work or advance in data science. However, having a degree or an advanced degree can be an advantage when applying for data science jobs. Additionally, it is common for companies to list a bachelor’s or advanced degree as a requirement for certain roles.

2. What Should I Do to Advance in My Data Science Career?

Once you land an entry-level position, it will take time and effort before you are entrusted with more responsibilities. You may need to do more than just handle your daily tasks well to show you’re ready to take on more. Being proactive and looking for more ways to contribute helps.

You should also be honing the core skills and ability to use various tools to tackle the complex problems you’ll encounter sooner or later along the data science career path. Domain or business knowledge, getting an advanced degree, and working on soft skills also help.

3. When Should I Consider Applying for More Senior Data Scientist Roles?

You could be ready for a senior data scientist role after just three years of experience, or it may take longer. Some signs that you may be qualified for a more senior role include regularly playing an active role in setting the organization’s data science strategy, being able to handle regular tasks with ease and little oversight, and other data scientists turning to you for help.

Data scientists are in high demand today, and there is huge potential to progress quickly along the path of a career in data science. Even though you’re likely to start off working on small tasks under the guidance of other data scientists, the scope of your work can expand rapidly, and you could also have greater autonomy in just a few years. With this seniority, you’ll earn more, have a greater say over the kinds of projects you work on, and even manage other professionals.

Interview Query plays a key role in helping data scientists progress in their careers. We offer tools to help you prepare for interviews, such as company interview guides and questions and access to experts who can tell you what to expect when preparing for data science interviews at different levels of seniority. We also provide salary information for data science roles at different companies and various levels of seniority.

Whether you’re just starting your data science career or think you’re ready for a more senior role, we hope this guide will help you get where you want to be.