Junior vs Senior Data Scientists: Salaries, Responsibilities, and Career Paths

If you are a data scientist and want to move up in your career then you must know the key differences between junior vs senior data scientists. Advancing to a senior data scientist role is not just about investing time and gaining relevant practical experience. It’s also about acquiring strong leadership skills to manage a team.

This article will explain the roles of a junior vs senior data scientist, examine the differences in salary, and explore how long it takes to become a senior data scientist.

What Is a Data Scientist?

A data scientist is a person who uses data to understand and explain the world around them and help companies make better decisions. They create model predictions and design algorithms for extracting big data, data analysis, and evaluation.

These professionals have a high level of analytical ability, statistical modeling, and mathematical skills. Data science is an interdisciplinary field, which means data scientists need to have a good understanding of computer science, software development, and machine learning.

Junior vs Senior Data Scientist: Areas of Responsibility

If you are a junior data scientist who plans to pursue a senior data science career, you need to be aware of the responsibilities of each role. Junior data scientists test data, polish data, and check and analyze models. Senior data scientists work on more complex tasks such as data management, predictive modeling, and implementing unprocessed data. 

Junior Data Scientist Job Description

• Ask questions. Juniors need to know how to ask questions and communicate efficiently with their senior leaders to enhance their fundamental data science knowledge in descriptive and predictive analytics. This will help develop their skills in data wrangling, machine learning, data analysis, math, and statistics.
• Extract data. The majority of a junior data scientist’s responsibilities revolve around extracting data from multiple data sources, cleaning the data, and loading the data to a centralized location.
• Analyze data. Juniors also analyze data sets, look for missing values, and test and polish input data for models. Juniors need to have strong database knowledge, like Oracle Database or MySQL to perform data analysis.
• Measure results. Junior data scientists choose the right predictive model to extract meaningful insights from the data. This allows them to effectively measure results and make suggestions to their seniors on how to improve them. 
• Provide final results. A junior data scientist presents their final results to a senior member of the team. If a senior data scientist requests adjustments, they must make the necessary changes.

Senior Data Scientist Job Description

• Mentoring. Seniors lead and mentor junior data scientists by providing guidance and support when needed. They review junior data scientists’ work, provide feedback on data analysis projects, and give advice on data science techniques. One of their main responsibilities is to teach and evaluate junior colleagues.
• Managing. Senior data scientists manage a team of data scientists and delegate daily tasks. This ensures that projects are completed successfully. Seniors also manage the timeline of a project and assure it is delivered on time.
• Monitoring. Senior data scientists monitor project execution and oversee the process. They are responsible for convening daily meetings, proposing solutions for deviations from the plan, and communicating with clients.
• Strategizing. Seniors are responsible for creating a clear agenda and for designing an analytical strategy. A senior data scientist is more concerned with success and putting in place a system to measure it than actually extracting data and results.
• Communicating. Senior data scientists have exceptional communication skills. They must be able to explain complex technical information about data models in an approachable way. Senior data scientists explain results and share actionable insights by communicating effectively with stakeholders, business owners, and clients.

Junior vs Senior Data Scientist Salaries 

The average data scientist salary greatly varies between junior and senior levels. Juniors learn how to build predictive models and how to analyze data. Seniors, on the other hand, have a better understanding of data science and a responsibility to communicate with the company’s board and stakeholders. Therefore, senior data scientists earn a higher annual salary. 

Junior Data Scientist Salary

According to PayScale, a junior data scientist earns an average salary of $85,167 per year. The salary range for junior data scientists is between $61,000 and $119,000 annually. This high starting salary reflects the knowledge, skills, and education requirements of junior data scientists. For example, they must have a deep understanding of programming languages like SQL and Python and use them to build scripts for visualizations and data models.

Senior Data Scientist Salary

A senior data scientist earns an average annual salary of $122,363, according to Payscale. This salary ranges from $83,000 to $171,000 per year. Senior data scientists have a lot of responsibilities that directly affect the productivity and financial success of a company. They also look after complex problems and report to board directors, stakeholders and clients.

How Long Does It Take to Become a Junior Data Scientist?

It takes about one year to learn the skills a junior data scientist needs. Once you have the required skills, you will need one or two years of experience before you become a junior data scientist. The skills you need to have include data analysis, statistical analysis, and machine learning.

How Long Does It Take to Become a Senior Data Scientist?

Typically, you must be working three or more years in the field to advance to a senior data scientist role. In most cases, you will have been working as a junior data scientist. However, it is possible to transition into a senior data scientist role from another field such as mathematics or engineering. Senior data scientists need advanced technical and business skills.

Career Path and Progression from Junior to Senior Data Scientist

1. Junior Data Scientists

In most cases, juniors perform routine tasks like cleaning bugs, analyzing data into a repository, or modifying a feature in an existing data science service. The whole idea is to learn data science tools like machine learning, Python, research, R, and SQL for model development or data analysis.

Therefore, to progress, you need to start asking questions that will help you better understand projects from start to finish. If you have an entry-level data scientist job, learning how to set up machine learning pipelines can help you advance. 

2. Mid-Level Data Scientists

Mid-level data scientists need less guidance because they know more about data science practices. They use machine learning more as a way to solve a problem and less like a statistical method. Mid-level data scientists also undertake more complex projects, apply prediction models, and build resilient data pipelines.

Data scientists need basic management and soft skills to advance to this position of data science. Lower-level data scientists seek advice from senior data scientists. So at this step, it’s important to expand your data science knowledge and advance your communication skills.

3. Senior Level Data Scientist

The senior data scientist position has an excellent understanding of the project scope and knows exactly how and where to prioritize applications in a data science service. They bring high data accuracy, quality, and comprehensive explanations of technical concepts to stakeholders. 

Senior data science roles can branch out in two different paths: individual contributor or management. To earn a promotion, a senior data scientist needs to have deep domain knowledge, advanced data skills, communication, and a high ability to mentor junior data scientists.

Individual Contributor

Individual contributor data scientists have excellent knowledge of how to solve business problems. They present findings to stakeholders to help them save money. To advance to this role you will need excellent communication and problem-solving skills. 

Data Science Manager

Data science managers know how to successfully manage and lead entire data science teams. Individual contributors and managers have similar responsibilities, but managers are also responsible for mentoring other data scientists. To get to this level you could upskill with a business strategy bootcamp or take a data science management training course.

Should You Become a Data Scientist?

Yes, you should become a data scientist because they are in high demand, pay extremely well, and offer a career in an interdisciplinary field. If you want to start a career as a data scientist then consider applying for a data science internship. You can also enroll in a data science bootcamp and learn in-demand analytical skills to get you job-ready in a short space of time.

The demand for data scientists is higher than ever, as an increasing number of industries are using data science algorithms to maximize their business. Today, data science helps industries such as government, transportation, construction, manufacturing, and finance.

Junior vs Senior Data Scientist Salaries FAQ