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What Does a Data Science Career Path Look Like in 2025 and Beyond?

It seems like in every industry you look, businesses are using data science to get ahead. When the Harvard Business Review ordained data scientist the “sexiest job of the 21st century,” it was a sign that data science would only become more ubiquitous in coming years. 

The prophecy has come true. In early 2022, when Babson College professor of information technology and management Thomas Davenport revisited his prediction, he found that, while there would surely be increasing differentiation in responsibilities as the field of data science continued to mature, it was safe to say that data scientists would continue to be in high demand. In fact, according to the US Bureau of Labor Statistics, the US data science job market will grow by 21% from 2021 to 2031, over fourfold the growth rate the BLS projects for all occupations.

It’s no surprise, then, that lots of people are keen to land a job as a data scientist — but what does a career actually look like in 2025? In this article, we’ll show you a typical data science career path, beginning with the kinds of educational programs that can get you started.

What is data science?

Before we get started, let’s clear up an important question: what actually is data science? It seems like a simple question, but with so much overlap with business analytics and machine learning engineering, it’s important to clarify.

According to IBM, data science is “a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. [It] encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.” A data scientist follows data through the data pipeline:

  • Identifying research interests and questions,

  • Engaging in data preparation to turn unstructured data (or “raw data”) into useable data,

  • Analyzing that data, often with the help of machine learning algorithms and models,

  • And using data visualization tools to persuasively communicate findings.

Now that we’ve covered what data science is and gotten a basic idea of what data scientists do (we’ll elaborate below), let’s dive into the typical data science career path.

What’s a typical data science career path?

1. Bachelor’s Degree or Bootcamp

Most aspiring data scientists start their journey in one of two ways: they get a data-centric bachelor’s degree or they attend a data science bootcamp. We’ll tackle the bachelor’s degrees first.

Data Science Bachelor’s Degrees

Historically, the best majors for those wishing to eventually enter data science roles have been those that allow students to gain training in advanced mathematics, programming, business, or data analytics, such as computer science, mathematics, finance, or information technology, sometimes with relevant minors.

Recently, however, more and more colleges and universities have been offering specific majors in data science. Students majoring in data science can expect to take courses in:

  • Fundamental and applied mathematics, including calculus, probability, statistics, and linear algebra

  • Computer science, including introductions to programming languages, software engineering, database systems, and operating systems and systems programming

  • Machine learning, including introductions to algorithms, deep learning, and data analytics

  • Core data science techniques, focusing on how to develop data science research questions, design projects, and execute them

  • Data management, including data mining, data engineering, and data visualization

  • The social sciences and humanities, according to individual interest and distribution requirements

A bachelor’s degree program will generally require a four-year commitment, but tuition can vary widely, especially between in-state tuition at public universities and tuition at elite private universities. In general, undergraduates can expect to spend between $10,000 and $60,000 on tuition per year before living expenses. According to EducationData.org, the current average cost of attendance for an in-state student living on campus at a public school is $102,828 over 4 years. At a private university, this total swells to $218,004.

If you’re interested in learning more about data science bachelor’s degrees, you can check out our guide to the best data science undergraduate colleges and universities.

Data Science Bootcamps

For those who already have a bachelor’s degree or those who don’t wish to invest the time and money into a bachelor’s degree program, data science bootcamps are increasingly a viable path to a data science career, offering data science courses of study measured in months, not years.

It’s important to note, however, that a bootcamp, by virtue of its reduced duration and less standardized credentialing, isn’t meant to substitute for a four-year degree, but rather provide core skills needed to land an entry-level position in the field.

Students in a data science bootcamp can expect to cover much of the same material as someone in a bachelor’s program, though in much less time and thus in much less detail. Fundamental skills in mathematics are generally taught through self-directed preliminary courses required to be completed before beginning the bootcamp. Once beginning the program proper, bootcamp curricula generally include:

  • Fundamental data analysis, often through a programming language like SQL, Python, or R

  • Statistical modeling, including regression analysis

  • Machine learning basics, including elementary algorithms and machine learning libraries like PyTorch and Pandas

  • Data visualization techniques

  • A capstone project to jumpstart your portfolio

  • One-on-one coaching

Bootcamp tuitions vary drastically depending on what organization is offering the program and in which modality. It’s not uncommon for three-month, full-time programs from the most well-respected providers to cost upwards of $15,000, though part-time, more self-directed bootcamps can cost considerably less. Some bootcamps offer financing, installment plans, or income-share plans where you pay back a fraction of your income once you get a job. Others have guarantees that you will get a job offer or you get your money back.

If you’re interested in learning more about data science bootcamps, you can check out our guide, where we have recommendations for programs that will get you started on your own data science career path.

2. Recruitment

Already while completing their first course of study, aspiring data scientists will start their job search. This might involve:

  • building experience and a relationship with a company through an internship in the case of a university undergrad,

  • working with job placement staff (specifically for bootcamps who have that service) 

  • building out professional networks,

  • working on independent data science projects to add to a portfolio,

  • and crafting a resume that highlights data science and relevant industry experience.

3. Junior Positions

While those with a bachelor’s degree in data science, applied mathematics, or computer science might enter as junior data scientists, many holding bootcamp certificates or bachelor’s degrees in unrelated fields will enter as data analysts. So what’s the difference?

Data Analyst vs Data Scientist

Typically, data analysts will have a narrower scope of work, performing more rudimentary analyses to identify trends from existing databases. According to Salary.com, the salary range for data analysts is $73,002 to $91,552.

Data scientists instead focus more on developing predictive models and other data tools, as well as performing more advanced analytics. According to Salary.com, the salary range for junior data scientists is $80,432 to $101,098.

The differences in the responsibilities of data analysts and data scientists become clearer when looking at the following real-world positions. You can also check out our deeper dive on the difference between the two.

Data Analyst, Service Operations - New York Life Insurance Co.

New York, NY

Responsibilities

  • Act as a reporting and data analysis advisor.

  • Serve as a SME (Subject Matter Expert) within the service operations team, regularly advise on proper dispositioning of reviews and how to leverage the tools for maximum efficiency and insights.

  • Guide team to analyze and opine on the effectiveness and trending and reporting as well as identify gaps and opportunities for improvement.

  • Manage sites, lists, and documents in SharePoint that meet individual line of business needs while also enabling data aggregation and reporting.

  • Collaborate with key stakeholders and business partners to ensure quality systems provide line of sight to the highest priority quality issues and needs.

  • Ensure alignment between the quality teams on the information collected.

  • Drive awareness and understanding of how to interpret potentially uncomfortable quality results at the individual, process, and organizational levels.

  • Influence front line business partners toward action to resolve quality issues.

  • Keep up with current best practices regarding risk management/internal auditing.

Qualifications

  • Bachelor’s degree preferred

  • 2+ years of experience in data collection & storage, SharePoint administration, or Power Platform applications 

  • Strong reporting and data analytics skills

  • Strong verbal and written communication skills 

  • Comfort and confidence in interaction with all levels of management

  • Ability to solve problems, multi-task and manage changing priorities

  • Proficiency in Microsoft Suite with strong Excel skills

  • Familiarity with Power Apps and Power Automate

  • Relevant insurance or securities knowledge is a plus

  • Microsoft Certified: Power Platform Fundamentals certification is a plus

  • Proficiency in Tableau Visualization creation is a plus

Salary range: $55,000–$85,000, depending on qualifications

Junior Data Scientist - Nissan

Smyrna, TN

Responsibilities

  • Utilize expertise in statistical and machine learning methods using tools such as R & Python and proficiency in big data (ex. Hadoop) & cloud infrastructures to perform tasks related to all aspects of model development.

  • Identify what data is available and relevant while leveraging new data collection and analysis processes.

  • Collaborate with internal and external SME’s to select relevant sources of information

  • Interface with IS/IT for data pipeline needs, collaborative data curation, certification, and appropriate data product sourcing.

  • Develop experimental design approaches to validate findings or test hypotheses.

  • Provide on-going tracking and monitoring of performance of decision systems and statistical models. 

  • Perform rapid prototyping & POCs (proofs of concept).

  • Evaluate and improve system operations by conducting systems analysis and ongoing continuous improvement of machine learning models & output.

  • Recommend changes in policies and procedures. 

Qualifications

  • BS in Data Science or related STEM field

  • At least 3 years of relevant experience in data analysis, data management, quantitative and qualitative research and analytics experience.

  • Proven experience in building, deploying, and maintaining analytics models. 

  • Experience in business needs assessments, prioritization techniques, and business case development desired. Prior automotive experience a plus.

  • Ability to find solutions to loosely defined business problems by leveraging structured, semi-structured & large unstructured datasets.

  • Ability to use appropriate SQL tools and techniques to explore given datasets or databases including data preparation, cleansing, and data product assembly.

  • Advanced knowledge of statistical and machine learning methods required. High proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.

  • Can write functional code using common programming languages (R and/or Python).

  • Builds models which can successfully determine whether relationships exist between various data & reach insights.

  • Ability to synthesize data, uncover inherent trends in the data, make recommendations about associated opportunities and implications to business performance, & communicate findings & recommendations to a variety of audiences.

Salary Range: $72,000–$92,100, depending on qualifications (Indeed estimate)

4. Data Science Master’s Degree

While those with only a bootcamp certification or a bachelor’s degree may be able to secure a junior role and gain promotion from an entry-level position to a mid-level data scientist role, frequently those interested in accelerating their progress down a data science career path will level-up their skills and gain exposure to new, more advanced areas of data science through a master’s degree.

Master’s degrees are also a great option for those who already have a bachelor’s degree in a STEM field — or even the social sciences or humanities — who are wishing to transition into data science. If they have existing work experience, often a master’s degree will allow them to enter the field through a more senior position.

While universities frequently offer full-time in-person master’s programs, there are more and more part-time and online options for students looking for the flexibility to continue working or care for a loved one while they study.

While the curriculum of a data science master’s degree will resemble that of a data science bachelor’s degree to an extent, generally courses will be more advanced, with fundamentals either not offered or required to be completed in advance of beginning the degree proper. DS master’s students can expect to take courses in:

  • Computer science, including advanced courses in machine learning and deep learning, data science algorithms, and software design

  • Statistics and linear algebra, including advanced courses in exploratory data analysis and visualization, statistical modeling, statistical analysis with SQL and R, and other applied mathematics

  • Data science principles, including research design, data management (data collection, data mining, data cleansing, database management, etc.), and data science ethics

  • Industry-specific electives such as Marketing Analytics, Sports Performance Analytics, Big Data in Finance, etc.

As with bachelor’s programs, tuition for data science master’s programs can vary widely, not only because of the difference between public university in-state tuition and private university tuition, but also because of the availability of online and part-time programs. EducationData.org pegs the current average cost of a master’s degree in science at $59,720.

Our guide to data science master’s programs has lots of useful information for those interested in graduate study in data science.

5. Mid-Level Data Scientist

Those with master’s degrees have a much higher chance of landing a mid-level data scientist position — in fact, some companies require that candidates hold a master’s degree at the time of applying. Mid-level data scientists are expected to have more experience, greater mastery over programming languages and machine learning libraries, and exposure to more advanced data science techniques and concepts.

In exchange for their higher qualifications, a mid-level data scientist’s responsibilities resemble those of an entry-level data scientist in form, but generally mid-level data scientists have far more control when it comes to big-picture issues like research scope and model design. Mid-level data scientists might also manage junior data scientists who will complete the more menial aspects of data mining, cleaning and analysis. Mid-level data scientists might also begin specializing in a particular industry or on a certain kind of product.

Data Scientist, Marketing & Online - Walmart

Sunnyvale, CA

Responsibilities

  • Design and develop algorithms and models to use against large datasets to create business insights. 

  • Participate in large data analytics project teams by serving as a technical lead for analytics projects. 

  • Lead small projects and work independently on solution development.

  • Make appropriate selection, utilization and interpretation of advanced analytical methodologies.

  • Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners.

  • Present recommendations in a confident manner in order to influence execution of recommendation.

  • Prepare reports, updates and/or presentations related to progress made on a project or solution.

  • Incorporate business knowledge into solution approach and effectively develop trust and collaboration with internal customers and cross-functional teams. 

  • Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects.

  • Build and maintain library of reusable algorithms for future use, ensuring developed codes are documented.

Qualifications

  • Master’s in a quantitative field (Computer Science, Math, Statistics, etc.) or equivalent work experience

  • 4+ years of experience in business intelligence and analytics

  • Experience in a modern scripting language (preferably Python)

  • Proficient running queries against data (preferably with Google BigQuery or SQL)

  • Proficient with data visualization software (preferably Tableau)

  • Proficient utilizing statistical techniques to identify key insights that help solve business problems

  • Knowledgeable in Prescriptive Modeling like optimization, computer vision, recommendation, search or NLP

The most qualified candidate for this position would earn $160,000 annually.

6. Senior Data Scientist

Senior data scientists are generally expected to have 5 to 10 years of experience as a data scientist, often in a specific industry. Continuing the trend beginning with the mid-level data scientist, senior data scientists generally engage in more project direction and personnel management than those in more junior roles, ensuring projects are designed and executed efficiently and effectively and serving as a key mentor for early-career analysts and data scientists.

When companies lay out responsibilities and qualifications for senior data scientists, they are generally far more specific because these companies have a more specific staffing need — either for a specific project, series of projects, or division of the company. The Senior Data Scientist position at Disney, below, requires significant experience and expertise in market science.

Senior Data Scientist, Marketing Science Team - Disney Media & Entertainment Distribution

New York, NY

Responsibilities

  • Build, sustain and scale econometric models (MMM) for Disney Streaming Services with support from data engineering and data product teams.

  • Quantify ROI on marketing investment, determine optimal spend range across the portfolio, identify proposed efficiency caps by channel, set budget amounts and inform subscriber acquisition forecasts.

  • Support ad hoc strategic analysis to provide recommendations that drive increased return on spend through shifts in mix, flighting, messaging and tactics, and that help cross-validate model results.

  • Provide insights to marketing and finance teams, helping to design and execute experiments to move recommendations forward based on company goals (e.g., subscriber growth, etc).

  • Support long-term MMM (et.al.) automation, productionalization and scale with support from data engineering and product.

  • Build out front-end reporting and dashboarding in partnership with data product analysts and data engineers to communicate performance metrics across services, markets, channels and subscriber types.

  • Onboard new talent and serve as strong mentor to analysts.

Qualifications

  • 5+ years of experience in a marketing science / analytics role with understanding of measurement and optimization best practices

  • Coursework or direct experience in applied econometric modeling, ideally in support of measure marketing efficiency and optimize spend, flighting and mix to maximize return on ad spend (i.e., MMM)

  • Exposure or understanding of media attribution practices for digital and linear media, the data required to power them and methodologies for measurement

  • Understanding of incrementality experiments to validate model recommendations and gain learnings on channel/publisher efficacy

  • Exposure to / familiarity with with business intelligence and data concepts and experience building out self-service marketing data solutions

  • Experience in SQL as well as statistical modeling platforms (Python, R, etc.)

The most qualified applicant for this position would earn $191,180 per year.

7. Executive Data Science Roles

Executive data science roles can have a variety of titles — VP of Data Science, Chief Data Scientist, Director of Data Science, etc. — and, as with senior data science roles, companies will generally prioritize industry experience and expertise and “fit” more than they would for a more junior role.

Data science executives generally focus less on the day-to-day of specific data science projects, instead focusing on managing part or all of a company’s data science operations. Often, these executives develop multi-year strategies for building out and improving a data science program. Accordingly, they require not only core data science skills, but also advanced management skills and a keen understanding of the direction of the field of data science as a whole.

Most data science executives have at least a master’s degree, with many also holding a PhD in data science.

Director, Data Science: External Data Intelligence - Capital One

New York, NY

Responsibilities

  • Lead the Bureau Data Strategy function of the External Data Intelligence team, a team of analysts, data scientists, engineers, and product owners.

  • Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of data. 

  • Build data engineering solutions through all phases of development, from design through development, evaluation, validation, and implementation. 

  • Flex your interpersonal skills to translate the complexity of your work into tangible business goals in meetings with upper-level and C-suite executives.

Qualifications

  • Bachelor’s degree plus 9 years of experience in data analytics, or master’s degree plus 7 years of experience in data analytics, or PhD (preferred) plus 4 years of experience in data analytics

  • At least 5 years of experience in Python, Scala, or R for large scale data analysis

  • At least 1 year of experience working with AWS

  • At least 3 years of experience managing people

  • At least 5 years of experience with machine learning

  • At least 4 years of experience with relational databases

The most qualified applicant for this position would earn $307,440 annually.

Other Paths

While the above career path is a typical one for a successful data scientist, it’s by no means the only path. Several other options are open to someone interested with data science. Check out our career guides to get the low-down on the following: