Employers in 2025 are looking to fill data scientist jobs, with our insights suggesting this trend will continue for the foreseeable future. So what does the role of data scientist entail and what are the key skills involved?
In this article, we delve into the skills you need to take your first steps toward a successful data science career. We also examine how the role is changing and the impact that new technology is having in this field.
A data scientist handles vast amounts of unstructured data which can come from a number of sources. The data scientist will translate these insights to define data-driven business solutions. Data scientists define solutions using machine learning algorithms and artificial intelligence (AI), among other methods.
Organisations are looking for people who will efficiently extract data and offer valuable insights. These insights allow businesses to take relevant action. James Milligan discusses this hiring trend in his blog focusing on the top 10 permanent tech jobs in 2025.
Both data scientists and data analysts work with data, but the roles differ in many important ways. For example:
Data analysts: The role of a data analyst typically involves interpreting existing data and providing businesses with actionable insights. Find out more about the skills you need for a data analyst role, and how to apply, in our guide.
Data scientists: The role of a data scientist is generally broader in scope than an analyst. Data scientists will often formulate questions designed to uncover insights, and predict outcomes. These questions often start as business-related queries, before being refined into data-specific inquiries. A job as a data scientist involves more advanced tools and models than an analyst might apply to derive insights.
The most useful skills a data scientist can have depends on the specific requirements of their role. We can split most data science roles into three core pillars:
Successful data scientists need to demonstrate strong analytical skills. A solid grasp of mathematics is a must. A degree or PHD is also advantageous, especially in the following subjects:
Proficiency with analytics tools will be useful for data science professionals. Examples of valuable data analysis tools include:
Ideal candidates for data science roles will also be fluent (or at least proficient) in programming languages such as:
An understanding of AI and machine learning models will also be vital when processing large data sets.
The commercial pillar of a data science role is more distinct, requiring a slightly different set of skills, including:
Candidates will benefit from an ability to communicate with a wide range of colleagues and stakeholders.
In addition to our three core pillars, prospective data scientists will also need to master certain soft skills. Soft skills aren’t job-specific, but they remain still essential to the role. For a successful data scientist, key soft skills include:
You can demonstrate your soft skills by providing employers with specific examples from previous roles. If you don't have specific data science experience on your CV, you can talk about educational experiences.
Developing your soft skills and coding abilities will make you a strong candidate for any potential data scientist position.
Entry level data scientist jobs tend to require industry-related skills. But some employers may be more concerned with finding the right candidate, who they can train or upskill. A lack of experience shouldn’t hold you back as long as you can demonstrate the fundamentals of the role you are applying for.
Employers will be looking for candidates with an ability to code. By learning to write any of the languages listed above, you’ll be off to a good start.
First and foremost, achieving the right education will lay a solid foundation for a career path in data science. A degree in mathematics or statistics will be highly advantageous, especially when applying for graduate schemes. However, there are graduate programs available that will train graduates in any subject to become a data scientist.
If you don’t have a degree, there are still many opportunities to take the first steps toward a data scientist role. You can build relevant skills by taking online courses, participating in bootcamps, and building a strong portfolio of projects. Most extracurricular training will help to demonstrate your abilities to potential employers.
Until recently, companies would hire a data scientist without a clear strategy or fixed goal for the data scientist. As the tech industry has grown, organisations are now better informed and more prepared regarding their data strategy.
Now businesses have a much clearer idea of the role that a data scientist can perform for them. As an applicant, be prepared for managers to set clear targets for your role. If you feel your goals are unclear, try asking about the strategy or long-term plan for the role in your interview.
A good data scientist will be able to adapt to advancements in technology. After all, change is a pivotal part of any data role. Machine learning and AI have become key technologies in data science, with these new technologies allowing for:
Constant change and technical advancements are critical for the ongoing application of data science across numerous sectors.
If you are quick to adapt to new technologies and seek a rewarding, impactful role, data science may well be the career for you. Enjoy the benefits of a high demand position, including potential financial success, driving business growth and real-world innovation.
Find your next role as part of a data science team today.
Martin Pardey Senior Business Director Hays Technology – South East UK
Martin Pardey is a Director for Hays Technology in the South East, the World’s largest specialist recruitment company. Martin has 15 years’ experience at Hays, specialising in the last few years in the Business Intelligence and Data Analytics sector.
