- Start Here: Software Engineer vs Data Scientist
- Software Engineer vs Data Scientist at a Glance
- What Software Developers and Data Scientists Do
- Education, Skills, and Graduate Degree Pathways
- Salary Comparison: Software Developer vs Data Scientist
- Job Growth and Employment Outlook
- Career Settings and Long-Term Direction
- Software Engineer vs Data Scientist: Which Career Fits You Better?
- Frequently Asked Questions
- Edited By Jeevan D'Souza
- Expert Reviewed By Soumen Chatterjee
Start Here: Software Engineer vs Data Scientist
Software engineers and data scientists both work with code, data, and complex technical problems, but the work they are hired to do is different.
Software developers design and build computer applications and systems. Their work centers on creating, improving, testing, and maintaining software.
Data scientists use analytical tools and techniques to extract insights from data. Their work can involve collecting and preparing data, developing algorithms and models, analyzing results, and communicating findings that help organizations make decisions.
Because BLS does not publish “Software Engineer” as the occupational unit used for this comparison, OMC uses Software Developers (SOC 15-1252) as the federal occupational match for the software-engineering side of this page.
For the federal-data comparison on this page, OMC uses:
- Software Developers — SOC 15-1252
- Data Scientists — SOC 15-2051
This distinction matters. BLS also publishes an Occupational Outlook Handbook profile combining Software Developers, Quality Assurance Analysts, and Testers , but that broader grouping should not be used as though it were the individual Software Developer occupation. Current BLS projections separately identify Software Developers as 15-1252 .
Both occupations typically require a bachelor’s degree for entry. BLS notes that some employers prefer software developers with a master’s degree, while some employers require or prefer data scientists with a master’s or doctoral degree. Neither occupation, however, has a master’s degree as its BLS-designated typical entry-level education.
The fundamental career choice is therefore less about which field is objectively “better” and more about the type of technical work you want to do:
Software development centers on building software. Data science centers on extracting information and insight from data.
This page compares the two careers using current federal occupational data and separates those sourced findings from OMC interpretation. It is part of OMC’s career and degree comparison library .
Software Engineer vs Data Scientist at a Glance
The current federal data reveal a useful tradeoff: Software Developers have the higher national wage benchmark and operate in a much larger occupational market, while Data Scientists have the substantially faster projected growth rate.
| Metric | Software Developer | Data Scientist |
|---|---|---|
| BLS SOC Code | 15-1252 | 15-2051 |
| Core Work | Design and develop software applications and systems | Use analytical tools and techniques to extract insights from data |
| Typical Entry-Level Education | Bachelor’s degree | Bachelor’s degree |
| May 2025 Employment | 1,687,890 | 262,440 |
| May 2025 Median Hourly Wage | $65.38 | $57.80 |
| May 2025 Median Annual Wage | $135,980 | ~$120,220* |
| 2024 Employment — BLS Projections Base | 1,693,800 | 245,900 |
| Projected Employment 2034 | 1,961,400 | 328,300 |
| Projected Growth, 2024–2034 | 15.8% | 33.5% |
| Projected New Jobs | 267,700 | 82,500 |
| Projected Annual Openings | 115,200 | 23,400 |
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025; BLS Employment Projections, 2024–2034.
*BLS publishes the Software Developer median annual wage of $135,980 directly. The Data Scientist figure of approximately $120,220 is an OMC annualized calculation using the May 2025 BLS median hourly wage of $57.80 × 2,080 hours and should not be presented as a separately published BLS annual median.
What the Numbers Show
Software development is the much larger occupation. BLS’s 2024 projections base includes approximately 1.69 million Software Developers , compared with approximately 245,900 Data Scientists . Software Developers are projected to generate about 115,200 openings annually , compared with about 23,400 for Data Scientists .
Data science is growing much faster in percentage terms. BLS projects Data Scientist employment to increase approximately 33.5% from 2024 to 2034 , compared with approximately 15.8% for Software Developers . Both are well above the 3% projected growth for all U.S. occupations over the same period.
Software Developers currently have the higher national wage benchmark. May 2025 OEWS reports median hourly wages of $65.38 for Software Developers and $57.80 for Data Scientists .
Those findings answer different questions.
A faster percentage-growth rate does not mean Data Science will produce more total openings. Likewise, a higher national occupational wage does not establish that every Software Developer will earn more than every Data Scientist.
OMC Finding
The current federal data do not produce one universal winner. Software Developers have the larger employment market, more projected annual openings, and the higher May 2025 national median wage. Data Scientists have the substantially faster projected 2024–2034 growth rate. The stronger career fit depends on whether you would rather build software systems or use quantitative methods to extract insight from data.
What Software Developers and Data Scientists Do
Software Developers and Data Scientists can both spend substantial time working with code and technical systems, but they use those skills toward different objectives.
The simplest distinction is:
Software Developers primarily build and improve software. Data Scientists primarily analyze data and develop models to identify patterns, make predictions, and support decisions.
What Does a Software Developer Do?
BLS describes Software Developers as professionals who design computer applications or programs. Their responsibilities can include analyzing user needs, designing software, developing components of applications or systems, planning how pieces of software work together, maintaining existing systems, and documenting their work.
Depending on the position, a Software Developer may work on:
- Web or mobile applications.
- Enterprise software.
- Backend systems and APIs.
- Cloud-based applications.
- Operating or network systems.
- Databases and software integrations.
- Software testing and maintenance.
- Security and performance improvements.
The exact work varies substantially by employer and specialization, but the common objective is creating and maintaining functional software systems .
Software development also involves more than writing code. Developers must understand requirements, make design decisions, test solutions, identify defects, collaborate with other technical and business teams, and maintain software as requirements change.
Source: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook — Software Developers.
What Does a Data Scientist Do?
BLS describes Data Scientists as professionals who use analytical tools and techniques to extract meaningful insights from data.
Their responsibilities can include determining which data are useful, collecting and organizing information, creating and testing algorithms and models, analyzing results, visualizing findings, and communicating conclusions to stakeholders.
Depending on the position, a Data Scientist may work on:
- Statistical analysis.
- Predictive modeling.
- Machine learning.
- Data preparation and exploration.
- Algorithm development and evaluation.
- Experimental analysis.
- Data visualization.
- Communicating analytical findings.
The work combines programming with mathematics, statistics, analytical reasoning, and knowledge of the problem being studied.
The objective is generally not simply to process more data. It is to use data to identify patterns, evaluate questions, develop models, and produce information that can support decisions or predictions .
Source: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook — Data Scientists.
Software Developer vs Data Scientist: Role Differences
| Work Dimension | Software Developer | Data Scientist |
|---|---|---|
| Primary Objective | Build and maintain software | Extract information and insight from data |
| Core Technical Activity | Software design and development | Data analysis and modeling |
| Programming | Central | Central |
| Statistics | Varies by role | Central |
| Mathematics | Important for some specialties | More central to the occupation |
| Machine Learning | Relevant to some Software Developer roles | Relevant to many Data Scientist roles |
| Software/System Design | Central | Can be relevant, but not the occupation’s primary focus |
| Data Preparation and Analysis | Relevant depending on application | Central |
| Model Development | Relevant in specialized roles | Common |
| Testing | Test software functionality and performance | Evaluate analytical methods and model results |
| Communication | Requirements, designs, technical decisions, and development work | Analytical findings, methods, visualizations, and implications |
| Typical Output | Applications, programs, systems, features, and software components | Analyses, models, algorithms, visualizations, and findings |
There is real overlap.
A Data Scientist may write substantial amounts of production-quality code. A Software Developer working on machine-learning products may build data pipelines, implement models, or work extensively with statistical and analytical systems.
The occupations are therefore not separated by whether someone codes .
They are better separated by what the code is primarily being used to accomplish.
OMC analysis: If you are most interested in designing systems, building applications, debugging software, and turning requirements into working products, Software Development is the more direct occupational fit.
If you are more interested in statistical reasoning, discovering patterns, testing analytical questions, building predictive models, and interpreting data, Data Science deserves closer consideration.
OMC Finding
Software Developers and Data Scientists share programming and problem-solving skills, but their central objectives differ. Software Developers primarily use code to create and maintain software systems. Data Scientists primarily combine code with statistics, mathematics, and analytical methods to extract insight and develop models from data. The better fit depends less on whether you enjoy coding and more on what you want your technical work to produce.
Education, Skills, and Graduate Degree Pathways
Both Software Developers and Data Scientists typically enter their occupations with a bachelor’s degree , according to BLS. The difference is less about one career universally requiring more education and more about the academic and technical foundations each occupation emphasizes.
Software Development is commonly associated with computer and information technology or related fields. Data Science draws more heavily from mathematics, statistics, computer science, and related quantitative disciplines.
Education Comparison
| Education Factor | Software Developer | Data Scientist |
|---|---|---|
| Typical Entry-Level Education | Bachelor’s degree | Bachelor’s degree |
| Relevant Academic Backgrounds | Computer and information technology or related fields such as engineering or mathematics | Mathematics, statistics, computer science, business, engineering, or related fields |
| Graduate Degree Required by BLS for Typical Entry? | No | No |
| Graduate Education | Some employers prefer developers with a master’s degree | Some employers require or prefer a master’s or doctoral degree |
| Academic Emphasis | Computing, programming, software development, systems, and related technical foundations | Mathematics, statistics, computer science, data analysis, and related quantitative foundations |
Source: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook — Software Developers and Data Scientists.
BLS identifies a bachelor’s degree as the typical entry-level education for both occupations .
That point is important because the existing federal evidence does not support a blanket conclusion that:
You need a master’s degree to become a Data Scientist.
BLS does note that some Data Scientist employers require or prefer a master’s or doctoral degree. It also notes that some Software Developer employers prefer candidates with a master’s degree.
Those statements establish that graduate education can matter in both labor markets. They do not establish a universal graduate-degree requirement or quantify how much a master’s degree improves hiring probability, salary, or career advancement.
How the Skill Profiles Differ
| Skill Area | Software Developer | Data Scientist |
|---|---|---|
| Programming | Central | Central |
| Software Development | Central | Relevant, but not the defining occupational function |
| System/Application Design | Central | Varies by role |
| Algorithms | Important | Important |
| Mathematics | Important, with depth varying by specialization | Central |
| Statistics | Varies by role | Central |
| Data Analysis | Varies by role | Central |
| Machine Learning | Relevant to specialized development roles | Common within Data Science |
| Problem-Solving | Central | Central |
| Communication | Important for explaining requirements, designs, and technical decisions | Important for explaining analytical findings and recommendations |
| Primary Technical Orientation | Building reliable software and systems | Analyzing data and developing analytical models |
Sources: U.S. Bureau of Labor Statistics Occupational Outlook Handbook; O*NET occupational profiles.
There is enough overlap that a person with strong programming ability could potentially find either field technically interesting.
The larger divergence is in the supporting skill set.
Software Development places greater emphasis on designing, constructing, testing, and maintaining software. Data Science places greater emphasis on mathematics, statistics, analytical methods, and extracting useful information from data.
OMC analysis: Students who enjoy programming should therefore avoid using “I like coding” as the deciding factor. Coding is relevant to both occupations.
A better question is:
Would I rather use programming primarily to build software systems, or combine programming with statistics and mathematics to analyze data and develop models?
When Does a Master’s Degree Make Sense?
A graduate degree should be evaluated against the specific role a student wants rather than treated as an automatic requirement for either career.
For Software Development, graduate study may make sense for students seeking deeper education in areas such as software engineering, computer science, systems, or another technical specialization.
Students considering that path can explore online master’s in software engineering programs or compare an MS in Computer Science vs MS in Software Engineering based on the type of graduate training they want.
For Data Science, graduate education can provide deeper preparation in statistics, machine learning, quantitative methods, and data-focused computing. BLS also specifically notes that some employers require or prefer candidates with a master’s or doctoral degree.
Students considering graduate study on the data side can compare an MS in Data Science vs MS in Data Analytics or specific programs such as Indiana’s MS in Data Science vs ASU’s MS in Data Science .
Federal occupational data do not establish that a master’s degree is universally required for either occupation or that graduate education produces a specific salary premium, advancement outcome, or financial return. Those outcomes depend on the role, employer, existing education and experience, program cost, and the type of work a student wants to pursue. Example questions like:
- Is required to become a Data Scientist nationally.
- Is required for advancement as a Software Developer.
- Produces a specific salary premium in either occupation.
- Guarantees access to senior or specialized roles.
- Has a universally positive financial return.
Those questions depend on the role, employer, existing education and experience, program cost, and the type of work the student wants to pursue.
Which Educational Path Fits Better?
| If You Prefer… | Path to Examine More Closely |
|---|---|
| Building applications and software systems | Software Development |
| Computer science and software engineering | Software Development |
| System design and software architecture | Software Development |
| Mathematics and statistics | Data Science |
| Statistical modeling and quantitative analysis | Data Science |
| Machine learning and predictive modeling | Data Science deserves closer consideration |
| Programming without heavy statistical emphasis | Software Development deserves closer consideration |
| Programming combined with substantial quantitative analysis | Data Science deserves closer consideration |
| Avoiding a career that universally requires graduate school | Both remain possible; BLS lists a bachelor’s degree as typical entry education for both |
The graduate-degree decision should come after identifying the career and specialization you want—not before it.
OMC Finding
BLS identifies a bachelor’s degree as the typical entry-level education for both Software Developers and Data Scientists. Data Science generally places greater emphasis on mathematics, statistics, and analytical methods, while Software Development places greater emphasis on building and maintaining software systems. Some employers prefer advanced degrees in both fields, and some Data Scientist employers require them, but current federal evidence does not support treating a master’s degree as a universal requirement for either career.
Salary Comparison: Software Developer vs Data Scientist
Software Developers currently have the higher national wage benchmark.
BLS May 2025 OEWS data report a median annual wage of $135,980 for Software Developers . For Data Scientists, the May 2025 national OEWS release reports a median hourly wage of $57.80 , equivalent to approximately $120,220 annually when multiplied by 2,080 hours.
| Salary Measure | Software Developer | Data Scientist |
|---|---|---|
| SOC Code | 15-1252 | 15-2051 |
| May 2025 Median Hourly Wage | $65.38 | $57.80 |
| May 2025 Median Annual Wage | $135,980 | ~$120,220 * |
| May 2025 Mean Annual Wage | $148,100 | $126,800 |
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025. Software Developer median annual wage is also published by the BLS Occupational Outlook Handbook.
*The Data Scientist annual median shown here is an OMC conversion of the BLS May 2025 median hourly wage of $57.80 × 2,080 hours. It should not be described as a separately published BLS annual median.
On these national measures, Software Developers have the higher wage benchmark. The directly reported median hourly wage is approximately 13.1% higher for Software Developers ($65.38 versus $57.80).
The difference in mean annual wages is approximately $21,300 , with BLS reporting $148,100 for Software Developers and $126,800 for Data Scientists .
These are occupation-level national estimates. They do not establish what a particular Software Developer or Data Scientist will earn, nor do they isolate differences attributable to education, experience, specialization, employer, industry, or location.
What the Wage Distribution Means
BLS wage distributions can provide more context than the median alone.
The important guardrail is that wage percentiles are distribution points , not career stages.
A worker at the 25th percentile is not necessarily “entry level,” and a worker at the 90th percentile is not necessarily senior, principal, executive, or a top performer. The data also do not establish a maximum earning potential for either career.
BLS’s current Software Developer profile reports that the lowest 10% earned less than $82,460 and the highest 10% earned more than $214,670 in May 2025.
For the salary comparison, the useful conclusion is therefore straightforward:
Current national BLS wage data favor Software Developers, but they do not establish a career-long earnings winner.
The data tell us how wages are distributed across workers in each occupation at a point in time. They do not track the same workers as their careers progress.
Should Salary Decide Between the Two Careers?
Salary is a legitimate advantage for Software Development in the current national data, but the difference should be weighed against the work itself.
| If Your Priority Is… | What the Current Data Show |
|---|---|
| Higher national median wage | Software Developer |
| Higher national mean wage | Software Developer |
| Guaranteed higher individual salary | Neither |
| Higher guaranteed long-term earnings | Not established |
| Higher maximum earning potential | Not established by BLS |
| Salary by a particular employer or location | Requires more specific data than the national occupational estimates |
OMC analysis: A student who genuinely prefers Data Science should not choose Software Development solely because the current national median is higher. Conversely, someone deciding between two otherwise equally attractive careers should treat the higher Software Developer wage benchmark as a real measurable difference rather than dismissing it.
OMC Finding
Software Developers currently have the higher national wage benchmark. BLS reports a May 2025 median annual wage of $135,980 for Software Developers, while the May 2025 median hourly wage for Data Scientists is $57.80, equivalent to approximately $120,220 annually using 2,080 hours. BLS also reports higher mean annual wages for Software Developers: $148,100 versus $126,800. These figures establish a current occupation-level wage advantage for Software Developers, but they do not establish seniority-based salaries, compensation ceilings, or which career will produce higher lifetime earnings.
Job Growth and Employment Outlook
Both Software Developers and Data Scientists have strong federal employment projections, but the advantage changes depending on whether you look at percentage growth or the number of jobs and openings .
BLS projects Data Scientists to grow substantially faster in percentage terms from 2024 to 2034. Software Developers, however, represent a much larger occupation and are projected to add more jobs and generate far more annual openings.
| Employment Measure | Software Developer | Data Scientist |
|---|---|---|
| SOC Code | 15-1252 | 15-2051 |
| Employment, 2024 | 1,693,800 | 245,900 |
| Projected Employment, 2034 | 1,961,400 | 328,300 |
| Projected New Jobs, 2024–2034 | 267,700 | 82,500 |
| Projected Growth | 15.8% | 33.5% |
| Average Annual Openings | 115,200 | 23,400 |
Source: U.S. Bureau of Labor Statistics, Employment Projections, 2024–2034.
Faster Growth vs More Job Openings
Data Science has the stronger percentage-growth outlook. BLS projects Data Scientist employment to increase 33.5% , from approximately 245,900 jobs in 2024 to 328,300 in 2034. That places Data Scientists among the fastest-growing occupations in the current BLS projections.
Software Development has the larger employment market. BLS projects Software Developer employment to increase 15.8% , from approximately 1.69 million jobs to 1.96 million. That translates to approximately 267,700 additional jobs , more than three times the projected numeric increase for Data Scientists.
The difference is even larger for annual openings:
- Software Developers: approximately 115,200 openings per year
- Data Scientists: approximately 23,400 openings per year
Annual openings are not the same as newly created jobs. BLS estimates openings resulting from occupational growth as well as workers leaving an occupation through transfers or labor-force exits.
This is why saying simply that “Data Science has a better job outlook” would be incomplete.
Data Science has the advantage in projected percentage growth .
Software Development has the advantage in occupational size, projected numeric job growth, and annual openings .
What’s Driving the Growth?
BLS expects continued demand for Software Developers as organizations expand software development for areas including artificial intelligence, Internet of Things applications, robotics, automation, computer security, and the growing number of products that incorporate software.
For Data Scientists, BLS connects projected growth to increasing demand for data-driven decisions and the expanding volume and potential uses of data. Organizations are expected to need Data Scientists to analyze information, improve business processes, develop products, and support decision-making.
These explanations support demand for the specific occupations . They do not establish which individual career is safer from layoffs, which is easier to enter, or which provides greater long-term job security.
Which Career Has the Better Employment Outlook?
It depends on the employment measure being compared.
| If You Prioritize… | Current BLS Advantage |
|---|---|
| Faster projected percentage growth | Data Scientist |
| More projected new jobs | Software Developer |
| More annual openings | Software Developer |
| Larger existing employment market | Software Developer |
| Guaranteed individual job security | Neither — not established by BLS projections |
OMC analysis: Students should not interpret Data Science’s 33.5% growth rate as evidence that Data Scientist jobs will be easier to obtain. Likewise, Software Development’s larger number of openings does not establish that an individual applicant will face less competition.
The projections measure expected occupational employment, not hiring difficulty or an individual’s probability of getting a job.
OMC Finding
Data Scientists have the faster projected growth rate at 33.5% from 2024 to 2034, compared with 15.8% for Software Developers. Software Development, however, begins from a much larger employment base and is projected to add about 267,700 jobs and generate 115,200 annual openings, compared with 82,500 additional jobs and 23,400 annual openings for Data Scientists. Data Science wins on growth rate; Software Development wins on employment scale and openings. Neither measure establishes individual job security or hiring difficulty.
Career Settings and Long-Term Direction
Software Developers and Data Scientists work across many of the same industries, including technology, finance, professional services, and other organizations that depend on software and data.
The bigger long-term difference is usually not where they work but what kind of technical responsibility they want to deepen over time .
Software Developers can build expertise around software architecture, applications, infrastructure, systems, security, cloud computing, or other development areas. Data Scientists can deepen their work in statistical modeling, machine learning, experimentation, analytics, and other data-intensive areas.
Neither BLS wage data nor employment projections establish a standard career timeline for either occupation.
Where Do Software Developers and Data Scientists Work?
BLS industry data show that neither occupation is limited to traditional technology companies.
Software Developers work across industries such as computer systems design, software publishing, finance and insurance, manufacturing, and other organizations that develop or depend heavily on software.
Data Scientists also work across multiple industries, including computer systems design, insurance, management of companies and enterprises, consulting, scientific research, and other data-intensive organizations.
This matters because choosing between the careers does not necessarily mean choosing between a technology company and a nontechnology company .
Banks, healthcare organizations, retailers, manufacturers, government organizations, consulting firms, and many other employers can employ technical professionals whose work falls within or near these occupational categories.
The exact mix of opportunities varies by industry and location.
How the Careers Can Develop
Career development can involve deeper technical specialization, broader responsibility, management, or movement into adjacent technical roles.
| Career Direction | Software Development | Data Science |
|---|---|---|
| Core technical specialization | Software development, systems, applications, architecture, infrastructure, cloud, security, and related areas | Statistics, machine learning, modeling, experimentation, analytics, and related data-focused areas |
| Technical depth | More complex software, systems, architecture, or specialized development problems | More complex analytical, statistical, modeling, or machine-learning problems |
| Adjacent technical work | Systems architecture, security, cloud/infrastructure, DevOps, data engineering, ML engineering, depending on skills and role | Analytics, machine learning, data engineering, ML engineering, research-oriented work, depending on skills and role |
| Management direction | Engineering or technical-team management can be possible | Data, analytics, or technical-team management can be possible |
| Executive/organizational leadership | Possible, but no standard progression is established | Possible, but no standard progression is established |
| Standard career timeline | None established by BLS | None established by BLS |
The adjacent roles in this table are OMC analysis of plausible technical directions , not BLS-established career progressions. Moving into any specific occupation depends on skills, experience, education, employer requirements, and the responsibilities of the position.
Technical Specialization vs Management
Neither career requires someone to move into management to continue developing professionally.
A Software Developer may choose to deepen expertise in software architecture, distributed systems, infrastructure, security, or another technical area rather than manage a team.
A Data Scientist may choose to deepen expertise in statistical methods, machine learning, experimentation, or specialized analytical problems rather than move into management.
Professionals in either field can also pursue positions involving team, project, or organizational responsibility.
Job titles and advancement timelines vary substantially by employer, and BLS employment and wage data do not track individual workers through their careers. Examples include:
Junior → Senior → Principal → Director → VP
Titles vary substantially by employer, and BLS employment and wage data do not track individual workers through their careers.
Can You Move Between Software Development and Data Science?
The occupations overlap enough technically that movement between them can be possible, but neither BLS nor OEWS data establish how common or easy such transitions are.
The practical difficulty depends on the direction of the move and the person’s existing skills.
OMC analysis: A Software Developer moving toward Data Science may need substantially stronger preparation in statistics, probability, data analysis, and modeling.
A Data Scientist moving toward Software Development may need deeper preparation in software engineering, application or system design, testing, maintainability, and production software development.
There are also adjacent roles—particularly machine learning engineering and data engineering —where skills associated with software development and data-focused work can overlap.
That does not make those roles automatic transition points. Their requirements vary by employer.
Which Career Provides More Flexibility?
There is no federal measure establishing that one occupation has greater career flexibility than the other.
Software Development has the much larger employment base and more projected annual openings, but those measures do not directly measure occupational mobility.
Data Science combines programming with mathematics, statistics, and analytical methods, but that combination does not establish that Data Scientists can move more easily into other careers.
OMC analysis: A better way to evaluate flexibility is to ask which set of adjacent technical directions you would actually want.
| If You Want to Build Toward… | Path to Examine More Closely |
|---|---|
| Software architecture and complex systems | Software Development |
| Cloud, infrastructure, or application engineering | Software Development |
| Statistical modeling | Data Science |
| Advanced analytics and experimentation | Data Science |
| Machine learning | Both can be relevant; the desired role matters |
| Machine-learning engineering | Can draw from both, with substantial software-engineering requirements |
| Data engineering | Can draw from both, depending on role and technical background |
| Technical management | Both can lead toward management responsibilities |
| A guaranteed executive pathway | Neither |
| A predictable promotion timeline | Neither |
The stronger long-term path is therefore not necessarily the one with the longest list of possible job titles.
It is the one whose core work and adjacent directions remain attractive as your responsibilities deepen .
OMC Finding
Software Developers and Data Scientists can work across many industries and can develop through technical specialization, management, or adjacent technical roles. Software Development generally provides deeper pathways around building software, systems, and architecture, while Data Science provides deeper pathways around statistics, modeling, machine learning, and analytical work. Federal data do not establish standard career ladders, promotion timelines, or a universal flexibility winner. The better long-term fit depends on which technical responsibilities you want to deepen rather than where you expect to be after a fixed number of years.
Software Engineer vs Data Scientist: Which Career Fits You Better?
Software Development and Data Science are both strong technical career paths. The current federal data do not identify one as universally better.
Choose toward Software Development if you want to build software and systems. Choose toward Data Science if you want to use statistics, programming, and analytical methods to extract insight and develop models from data
The clearest difference is the work itself:
Current labor-market data add useful context to that decision. Software Developers have the higher national wage benchmark, larger employment base, and more projected annual openings . Data Scientists have the faster projected percentage growth rate .
Consider Software Development If…
Software Development may align better if you:
- Want building applications, software, or systems to be the center of your work.
- Enjoy programming but do not want statistics and quantitative modeling to dominate your career.
- Are interested in software architecture, systems, cloud, infrastructure, security, or related engineering directions.
- Prefer designing and implementing technical solutions over analyzing data to answer questions.
- Value entering a much larger occupational market with more projected annual openings.
- Give meaningful weight to the higher current national wage benchmark.
BLS projects approximately 115,200 annual openings for Software Developers from 2024 to 2034 , compared with approximately 23,400 for Data Scientists .
Software Developers also have the higher current national wage benchmark, with a May 2025 median annual wage of $135,980 .
Those are legitimate measurable advantages.
They do not establish that Software Development is easier to enter, more secure, or more lucrative for every individual.
Consider Data Science If…
Data Science may align better if you:
- Enjoy mathematics and statistics in addition to programming.
- Want analyzing data to be a central part of your work.
- Are interested in statistical modeling, machine learning, experimentation, or predictive analysis.
- Prefer discovering patterns and answering questions with data over primarily building software products.
- Want to develop deeper quantitative and analytical expertise.
- Give meaningful weight to the occupation’s substantially faster projected growth rate.
BLS projects 33.5% employment growth for Data Scientists from 2024 to 2034 , compared with 15.8% for Software Developers .
That gives Data Science the stronger current federal percentage-growth outlook .
It does not establish that Data Scientist positions are easier to obtain or that the occupation offers greater individual job security.
Final Decision Framework
| If Your Priority Is… | Direction to Consider | Why |
|---|---|---|
| Building applications and software systems | Software Development | This is the occupation’s central function |
| Statistics and quantitative analysis | Data Science | These are central to Data Scientist work |
| Software architecture and systems | Software Development | More directly aligned with Software Developer responsibilities |
| Statistical modeling and experimentation | Data Science | More directly aligned with Data Scientist responsibilities |
| Machine learning | Depends on the role | Data Science and software-oriented ML roles can emphasize different skills |
| Higher current national wage benchmark | Software Developer | Current May 2025 BLS wage data favor Software Developers |
| Faster projected percentage growth | Data Scientist | 33.5% vs 15.8% from 2024–2034 |
| More projected annual openings | Software Developer | About 115,200 vs 23,400 |
| Larger occupational market | Software Developer | Approximately 1.69 million Software Developers in the 2024 projections base vs 245,900 Data Scientists |
| Career without a universal master’s requirement | Both | BLS lists a bachelor’s degree as typical entry-level education for both |
| Guaranteed higher long-term earnings | Neither | Federal occupational data do not establish lifetime earnings |
| Greater job security | Neither established | BLS projections do not measure individual job security |
| Predictable path to executive leadership | Neither | No standard progression is established by the federal data |
If You Like Both Coding and AI
Interest in coding or artificial intelligence does not resolve the choice by itself.
Software Developers can work on AI-enabled applications, infrastructure, platforms, and systems. Data Scientists can work with machine learning, algorithms, modeling, and data used in AI-related applications.
OMC analysis: The more useful distinction is which side of the work attracts you.
If you primarily want to engineer the software and systems through which technology operates , Software Development deserves closer consideration.
If you primarily want to work with data, statistical methods, models, and analytical questions , Data Science deserves closer consideration.
Some careers, particularly machine-learning engineering, can draw heavily from both areas. Students targeting those roles should evaluate the actual skill and education requirements of those positions rather than assuming either broad occupation is automatically the correct route.
The Decision in One Question
If salary, growth, and technology interest still leave you undecided, ask:
Would I rather spend my career becoming better at building software systems or becoming better at extracting information and developing models from data?
If the answer is building software , Software Development is the more direct fit.
If the answer is statistics, modeling, and analytical work with data , Data Science is the more direct fit.
If the answer is both , investigate the specific roles at the intersection—such as machine-learning engineering—before choosing an educational path.
Students considering graduate education can also compare an MS in Data Science vs MS in Software Engineering to see how the corresponding graduate-degree paths differ.
OMC Finding
Software Development has the measurable advantage in current national wage benchmarks, employment scale, projected new jobs, and annual openings. Data Science has the advantage in projected percentage growth. But those labor-market differences should support—not replace—the career decision. Software Development is the stronger fit if you want to build software and systems; Data Science is the stronger fit if you want statistics, modeling, and data analysis to be central to your work. Neither federal wage nor projection data establish a universal winner for long-term earnings, job security, or career advancement.
Frequently Asked Questions
Current national BLS data favor Software Developers. BLS reports a May 2025 median annual wage of $135,980 for Software Developers . For Data Scientists, the May 2025 median hourly wage is $57.80 , equivalent to approximately $120,220 annually using 2,080 hours. These are national occupational estimates and do not mean every Software Developer earns more than every Data Scientist.
Data Science is growing faster in percentage terms. BLS projects Data Scientist employment to grow 33.5% from 2024 to 2034 , compared with 15.8% for Software Developers . Software Development, however, is the much larger occupation and is projected to generate about 115,200 annual openings , compared with approximately 23,400 for Data Scientists .
There is no federal measure showing that one career is inherently harder than the other. The technical emphasis differs: Data Science generally requires greater emphasis on statistics, mathematics, modeling, and data analysis , while Software Development focuses more heavily on programming, software design, systems, testing, and maintainability . Which feels more difficult depends largely on a person’s strengths and interests.
Not universally. BLS identifies a bachelor’s degree as the typical entry-level education for Data Scientists , although it notes that some employers require or prefer candidates with a master’s or doctoral degree. The education required for a particular position therefore depends on the employer and the type of Data Science work involved.
It can be possible, but there is no standard transition established by BLS. OMC analysis: A Software Developer moving toward Data Science may need additional preparation in areas such as statistics, probability, data analysis, and statistical or machine-learning modeling. The amount of additional preparation depends on the person’s existing technical background and the Data Scientist role being targeted.
Both can lead to AI-related work, but from different directions. Software Developers may build the applications, infrastructure, and systems that use AI, while Data Scientists may work more directly with data, algorithms, statistical methods, and machine-learning models. For roles such as machine-learning engineering, skills from both software engineering and data-focused disciplines can be relevant.