Explore the key differences between software engineers and data scientists – from daily tasks to required skills, salaries, and career growth. Learn which tech path suits your strengths and goals, and how to transition between the two roles.
Job seekers thinking about their future careers in tech may be trying to decide between becoming a software engineer or a data scientist. While the two roles share skills in areas such as coding and problem-solving, the core distinction is that software engineers generally focus on building and maintaining systems, while data scientists analyse data to identify patterns, generate insights and solve problems.
So, when comparing a software engineer to a data scientist, which is better and who earns more? The answer depends on your skills, interests and career goals. For professionals interested in defence, government and intelligence, it’s also worth considering how each role operates within the security cleared job market.
Data science and software engineering both offer strong opportunities, but the day-to-day work and skills required can be very different. Here’s how the two careers compare side-by-side:
| Software engineer | Data scientist | |
| Core focus | Building and maintaining software and systems | Analysing data and developing models |
| Typical UK salary | £30,000–£75,000 | £32,000–£83,000 |
| Key skills | Programming, system design, cloud platforms, databases | Statistics, Python, SQL, machine learning |
| Typical degree | Computer science, software engineering or related subject | Data science, statistics, maths, computer science or related subject |
| Best suited if… | You enjoy building things and solving technical problems | You enjoy analysing information and finding patterns |
These salary ranges are a guide rather than a guarantee and can vary considerably according to experience, location, employer and specialism.
The most crucial differences between software engineering and data science are in terms of responsibilities. Both roles require creativity and problem-solving skills, as well as the patience to troubleshoot problems, but here’s where they vary.
A typical software engineering project could be the creation of a web app or new software platform. Within security cleared roles, software engineers may also develop and maintain secure systems and applications used across government, defence and other sensitive environments. Their day-to-day responsibilities include:
A data scientist, meanwhile, could work on predictive modelling, sentiment analysis or identifying patterns within large datasets. In government and intelligence environments, this may involve analysing complex information to support evidence-based assessments and decision-making. A typical day as a data scientist may include:
Many IT skills can be useful for careers in both software engineering and data science, but it’s important to hone your training in line with one or the other. A better understanding of which specific skills are required for each can help you decide which career you’re best suited for.
Software engineer employers typically look for skills such as:
For data scientist roles, required skills usually include:
Transferable skills like teamwork and communication will be useful in both software engineering and data scientist roles, as they’re required for successful career growth in tech. These attributes are crucial for ensuring members of IT departments work well together and can explain complex technical information clearly.
A good place to start your education is a computer science, software engineering, data science or other related degree, which can form a strong basis for your future career. For data scientists, degrees with a strong statistical or mathematical component may also provide a useful route into the profession.
However, a traditional university degree is not the only possible entry path. Apprenticeships, technical training and graduate schemes can also provide routes into technology careers, while practical projects and a strong portfolio can help demonstrate your abilities to prospective employers.
You can build on your skills with relevant professional certifications. Software engineers working with cloud technologies could consider certifications from providers such as Amazon Web Services (AWS) or Microsoft, including AWS Certified Developer – Associate and Microsoft Certified: Azure AI Cloud Developer Associate. Those specialising in secure software development may find the Certified Secure Software Lifecycle Professional qualification useful.
For professionals working with data, Google Cloud’s Professional Data Engineer Certification can demonstrate knowledge of areas including data pipelines, data modelling and machine learning.
Both software engineering and data science continue to provide career opportunities across the UK, but salary and progression can differ according to your experience, technical specialism, location and employer.
The National Careers Service currently gives a typical salary range of around £30,000 for a starter software developer to £75,000 for an experienced professional. Software engineers and developers can work across almost every sector, with opportunities to progress into senior software engineer, technical leadership, architecture, DevOps and other specialist roles.
Data science also has a wide range of applications as organisations increasingly use data, artificial intelligence and machine learning to inform decisions. Salaries can range from approximately £32,000 for a starter data scientist to £83,000 for an experienced professional. Career progression can include senior or principal data scientist roles, strategy positions, research and specialist AI or machine learning work. Pay is also dependent on location, for example, salaries for software engineers in London can be higher than in other parts of the UK.
These figures suggest that experienced data scientists can have a higher upper salary range, but they don’t mean a data scientist will always earn more than a software engineer.
There are also opportunities for both professions within government and other organisations handling sensitive information. For candidates who already hold or are eligible to undergo security vetting, defence, intelligence and public-sector technology projects can provide another route for career development.
Software engineers and data scientists can both work in security cleared environments, including government, defence, national security and intelligence. However, the type of clearance required depends on the specific post and the assets or information the individual will access, rather than simply their job title.
The Baseline Personnel Security Standard is used for pre-employment screening for individuals with access to government assets, although it is not itself a formal security clearance. Some software engineer and data scientist positions may require formal national security vetting such as Security Check or Developed Vetting, depending on the sensitivity of the work.
A security cleared software engineer might design, develop, test or maintain applications and systems for government departments, defence contractors or intelligence organisations. Depending on the role, this can include secure software development, cloud infrastructure, systems integration or other work involving sensitive government assets.
Security cleared data scientists can use statistical analysis, machine learning and artificial intelligence to work with complex datasets. Within government and national security environments, these skills may support activities such as intelligence analysis, forecasting and identifying patterns in large volumes of information.
Explore the latest software engineer jobs and data scientist roles on SecurityClearedJobs.com to see what employers are currently looking for.
Making the choice between being a software engineer and a data scientist should be a combination of looking at the differences and talking to people already in the role. Weighing up how many of your skills, interests and career goals align with each job is a good place to start.
If you enjoy creating applications, designing systems and turning requirements into functioning technology, software engineering may suit you. If you’re more interested in statistics, finding patterns in information and using data to answer questions or make predictions, data science may be a more natural fit.
Neither software engineering nor data science is inherently harder than the other. Software engineering can require deep knowledge of programming, system architecture and the software development lifecycle, while data science can demand more advanced statistics, mathematics and machine learning knowledge. Which feels harder will depend largely on your existing abilities and interests.
If salary is an important consideration, remember that job title alone does not determine what you will earn. Experience, specialist skills, sector, location and security clearance requirements can all affect remuneration.
These careers provide skills that are easily transferable, allowing you to change between software engineering and data science. Here’s how to shift between the two roles:
Yes. There is considerable overlap between the two careers, particularly in programming and problem-solving. Software engineers moving into data science will usually need to strengthen their statistics, machine learning and data analysis skills, while data scientists moving into software engineering may need more experience in system design, software development and testing.
Not necessarily. Computer science and software engineering degrees are common routes into software development, while data scientists frequently have backgrounds in data science, maths, statistics, computer science or other numerate subjects. Apprenticeships, training programmes and practical experience can also provide alternative entry routes.
Neither career is harder. Software engineering tends to place greater emphasis on programming, software architecture and building reliable systems, while data science generally requires stronger statistics, mathematics, data analysis and machine learning skills. Your own background and strengths are likely to determine which you find more challenging.
UK National Careers Service figures currently place software developer salaries at around £30,000 to £75,000 and data scientist salaries at around £32,000 to £83,000. This gives data science a higher upper range, but actual salaries for either career depend on factors including experience, specialist skills, employer and location.
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