The data scientist career path involves progressing from entry-level analyst roles, gaining more responsibility and leadership roles as you rise through the ranks. Data scientists use a mix of data analytics and business intelligence to drive smarter business decisions and solve complex problems. Eventually, a data scientist may need to choose between focusing on the data and technical side or the business side.
Career Path for Data Scientists
Progressing through a data scientist career, you typically have two routes you can take.
“You either deliver more and more technically as well as mentoring the staff and reviewing their deliverables,” says Xena Ugrinsky, CTO and co-founder of Pilot Wave Holdings Management. “Or you go the more business-focused route — managing the projects end to end, becoming the liaison to the business team.”
Data science exists at the intersection of technology and business, so it makes sense that higher-level roles may require focusing on one side more than the other.
But data science and the job title data scientist are umbrella terms. The career path to a data scientist role often involves starting in an entry-level position, with the job title data scientist or analyst, and carrying that title throughout your career with different levels (e.g., I, II, III) or amendments (e.g., junior, senior, lead, managing) added.
However, you can also hold a variety of job titles throughout your career while still doing the job of a data scientist. For instance, you could start your career as a data analyst, progress to the title of data scientist, and then become a director of data science and, eventually, a chief information officer (CIO).
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There are also ways to specialize throughout a data science career by focusing on one area, and having a specialization can be a great advantage.
“Become an expert in a specific domain or industry, such as finance, health care, or marketing,” says Vanja Djuric, associate professor of analytics at the University of Akron. “This can help you gain visibility and recognition for your contributions.”
Regardless of your level of seniority, specialization, or job title, there’s one step you can take that will always help you progress through your career: “Show the impact of your work by quantifying the results of your projects and presenting them to stakeholders,” says Djuric.
Not only will this give you visibility across your team, but it will also show your value to the company and can give you leverage when it comes to making transitions, landing promotions, or seeking new employment.
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Entry-Level Data Science Roles
Typical job titles: junior data scientist, junior data analyst, data scientist, data analyst, analyst, data analyst I, data scientist I, business analyst, business intelligence analyst
Entry-level roles as a data scientist usually involve executing tasks delegated by senior and lead scientists. The goal in these roles is to learn, explore areas of interest, and solidify your analytical skills. This is also the time to grow your technical skills in languages like R and Python.
Soft skills are especially important in entry-level positions. Ultimately, many of the daily duties of a data scientist are hard skills that can be taught along the way. It is vital to show you can work within a team, be willing to learn, listen actively, and communicate effectively. These skills are invaluable for career progression.
“Promotion criteria for most junior data scientist positions are a high degree of ownership, independent thinking, and the impact your work has brought to the business unit you are working with,” says Dushyant Sengar, director of data science at BDO USA.
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Mid-Level Data Science Roles
Typical job titles: data scientist, senior data scientist, data architect, data engineer, data mining engineer, senior business analyst,
Mid-level data scientist positions may involve many of the same duties or responsibilities as entry-level roles, with an added layer of seniority and ownership.
“There are overlaps to entry-level labels,” says Ugrinsky, “but usually just with the word manager [or senior] added.”
In mid-level positions, you are expected to have a stronger understanding of how the business can use data to overcome obstacles and the ability to take a problem and determine the right approach to solving it.
By this point in your career, your role may involve “working without supervision, managing a team, reviewing team output and being responsible for their quality, participating in higher level strategy decisions, and solution design,” says Ugrinsky.
You also should have enough experience to know whether you prefer the technical or business side of data science. If you prefer the technical side, you can begin shifting into engineering or architecture roles. To do this, you must show a strong understanding of coding, building Extract, Transform, Lead (ETL) pipelines, and planning data storage structures. Many certificates or bootcamps can help you learn, enhance, and prove your skills.
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To focus more on the business side, you need to show you understand how the business works, how data plays into business decisions, and how the company acts as part of the larger economy. Pursuing a master of business administration (MBA) degree or a specialized certificate can help show your business acumen.
Promotion into senior-level data scientist positions requires “developing technical skills to a high level of expertise, taking on challenging projects and demonstrating leadership potential,” says Ugrinsky.
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Senior-Level Data Science Roles
Typical job titles: lead data scientist, principal data scientist, director of data science, vice president of data science, chief data scientist, chief information officer, chief technology officer, chief operations officer
In senior data scientist positions, you must have a high level of ownership and a track record of managing crises and leading projects. You also need to show you can hire and build a competent team of data scientists, analysts, and engineers.
Additionally, senior-level data scientists often need to work alongside C-suite executives and other business leaders, so they need to bridge the gaps between the technical, analytical, and business aspects of data science, communicating findings clearly to stakeholders.
“Coaching and mentoring junior or mid-level data scientists to navigate day-to-day project problems or long-term career options is another set of responsibilities they need to take care of,” adds Sengar.
At this point in a data scientist’s career, they have proven themselves adaptable, flexible, and knowledgeable, with strong leadership skills. These attributes can eventually lead data scientists to become C-suite executives themselves, like chief technology officers (CTOs), chief information officers (CIOs), and chief operations officers (COOs).
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Data Science Career Path: Education, Certifications, and Skills
You typically need at least a bachelor’s degree to qualify for entry-level data scientist positions. Most employers look for degrees in analytical or quantitative disciplines like math, economics, statistics, computer science, information technology, or finance.
When looking to progress into higher-level roles, Djuric recommends students “pursue additional education or certification programs that can enhance your skills and knowledge in data science, such as a Master’s degree in data science or a certification in machine learning.”
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An MBA, too, can be a great way to boost your resume and help you move into more senior positions. Additionally, while an MBA can give you an in-road to focusing on the business side of data science, your undergraduate degree major can also give you a way to specialize early.
“Having industry-specific knowledge or experience can be beneficial, particularly for data scientists working in regulated industries such as health care or finance,” says Djuric.
So, if you’re passionate about data science and medicine, there are ways to set yourself up for that career path during college.
Professional certificates and coding bootcamps are excellent options for growing specific skill sets and boosting your data science career, regardless of your background. For example, if your degree is in business, but you want to transition into data engineering roles, taking a bootcamp in coding can be a great place to start building those skills.
Early-career certifications that can help you land entry-level roles or establish credibility early in your career include:
- SAS (Statistical Analysis System) Certified Data Scientist
- SAS Certified Advanced Analytics Professional
- Open Certified Data Scientist
- Microsoft Azure AI Fundamentals
- IBM Data Science Professional Certificate
These certifications and certificates are suitable for those with little to no experience in data science who are looking for ways to hone their skills. For those with experience (5+ years) as a data scientist, certifications are available to prove a higher degree of knowledge, skill, and technological ability.
Some advanced certifications in data science include:
- CAP (Certified Analytics Professional)
- DASCA Senior Data Scientist
- DASCA Principle Data Scientist
- Columbia University Certification of Professional Achievement in Data Sciences
These lists are far from exhaustive. However, remember to look for certifications only from reputable companies and institutions.
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“The importance of communication and presentation skills, also known as storytelling, cannot be stressed enough,” says Sengar.
Analyzing data is one thing, but using that data effectively and telling a story with it is a key skill for data scientists.
Additionally, because data science is an interdisciplinary field involving many players and departments, data scientists need to “work effectively with cross-functional teams and stakeholders, such as data engineers, business analysts, and executives,” says Djuric.
Other vital interpersonal and soft skills for data scientists include:
However, data scientists also need a plethora of technical or hard skills, such as:
- Applied mathematics
- Programming languages like Python and R
- SQL (structured query language)
- Databases and NoSQL databases
- Data visualization programs like Tableau and PowerBI
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Data Scientist Salaries
Regardless of location, experience, background, or specialization, data scientists make a median annual salary of $103,500, according to the U.S. Bureau of Labor Statistics (BLS). Similarly, across all levels and industries, wages reported on Glassdoor show an estimated salary range of $131,000 to $189,000 per year, including bonuses, stock sharing, profit sharing, and commission.
A data scientist’s salary may change drastically depending on how much experience they have, though.
|Years of Experience
|Estimated Average Salary Range
|0 to 1 year
|$93,000 to $147,000
|4 to 6 years
|$112,000 to $179,000
|$149,000 to $243,000
What if you don’t spend your career with just a data scientist job title, though? The principle stays the same — more seniority means more pay — but the upper end of the scale is much higher than for those with the title of data scientist alone.
For example, someone’s career could progress like this:
- Start an entry-level position with the title data scientist and an estimated pay range of $131,000 to $189,000
- Earn a promotion to lead data scientist after a few years of experience and have an estimated pay range of $178,000 to $266,000
- Progress into a director of data science role with an estimated pay range of $193,000 to $298,000
- Become a senior director of data scientist and have an estimated pay range of $222,000 to $350,000
Certain cities, like San Francisco and Seattle, may offer higher pay, too. Additionally, big tech companies and banks often have higher salaries since they rely heavily on data science and need top-notch scientists and analysts.
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Job Outlook for Data Scientists
The BLS predicts that the employment of data scientists is expected to grow 35% between 2022 and 2032, which is a significantly faster growth rate than the average across all occupations.
Even as data scientists become more in demand, it’s crucial to remember that “data science is a rapidly evolving field, and it is important to stay up to date with the latest tools, techniques, and trends,” says Djuric.
To stay ahead of the curve, successful data scientists should expect to continually learn and explore new and emerging technologies.
Additionally, because data science exists in both the tech and business spaces, there are some ways that machine learning systems and artificial intelligence (AI) will create paths for non-data scientists to assume data science roles.
“The easier the tools become as a facilitation for data cleansing, analysis, machine learning and visualization, the more opportunity there will be for non-data scientists to occupy these roles,” says Ugrinsky.
This means that there could be more room in the future for professionals with very different backgrounds and skill sets to work in data science. New career paths into and out of data science roles may develop as the data and business landscapes shift.
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