The way businesses make decisions has changed. They no longer rely on gut feeling, instinct, or even experience. Now data drives every decision and every conversation. That means businesses are more reliant than ever on those individuals who can collect, analyse, interpret and apply data.
With more businesses embracing technological innovation, there’s a knock-on impact on data and, in turn, data science. Within Europe, 55% of large enterprises adopted AI technologies and that figure will increase across 2026. That leaves a raft of opportunities for data scientists, ML engineers, data analysts, and those looking to break into the industry.
The Data Science Market in 2026
Across Europe, the demand for data scientists is unprecedented. The widespread adoption of AI is driving investment in data teams to capitalise on new technologies. Data scientists come into their own when it comes to working at an advanced level, using machine learning, statistical algorithms and tools such as PyTorch and Apache Spark to solve complex issues.
The London School of Economics places Data Scientists as the second most sought-after profession in 2026, with UK demand up 25% from 2025. When you look at the explosion of data across all functions and industries, that increase isn’t surprising. The global data science and analytics market reached $178.5 billion last year and is still continuing to grow.
While AI/ML may be automating some tasks, new roles in analytics, workflows, and data are emerging that fall to data scientists.
Skills Employers Are Prioritising
Employers prioritise two skill categories in 2026: technical capabilities and soft skills that bridge technology with business impact.
Soft skills include:
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Communication
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Ethical AI awareness
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Strategic planning
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Business acumen
They’re the skills that help improve user experience and also blend new technologies seamlessly into a business.
When it comes to technical skills, there are some clear front-runners that employers are actively looking for:
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Programming: Python, R, SQL
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ML/AI: TensorFlow, PyTorch, Scikit-learn
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Cloud: AWS SageMaker, Azure ML, GCP AI Platform
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Data engineering: Spark, Hadoop
Not all of the above will be relevant to every data science role, so while having a general awareness is useful, specialising in a few specific areas will improve your employability.
Underneath AI/ML, there are fundamental skills which are crucial for all data scientists; skills such as statistical modelling, data wrangling and visualisation. Those looking to progress in data fields need to ensure they have a strong foundation on which they can build their specific skill set.
Certifications to master
While tech moves quickly with new developments every day, having a baseline understanding is key. With so many certifications in cloud and ML platforms available, start by focusing on the right skills for the sector you’re currently in, or would like to move into.
Here’s a handy overview to get you started.
How Candidates Can Stand Out to Hiring Managers
Candidates can stand out to hiring managers by showcasing their technical skills (and qualifications to prove them) along with their demonstrable experience in delivering projects utilising those skills.
Candidates who want to stand out should start with their portfolio, keeping track of the real-life projects they’ve been involved in and the measurable impact that the project had i.e. improved model accuracy by 15%.
It’s the result which highlights candidates who can make the connections between the tech and the business, and that’s what employers are looking for.
An up-to-date, scannable CV is critical to standing out. Here’s a quick checklist to make sure it’s up to scratch:
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Stand alone achievements section laying out measurable impact and your specific role in achieving that.
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Remove unnecessary information - we don’t need to know about jobs older than 10 years. Keep your CV sharp, simple and concise.
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Weave keywords that are naturally relevant to the opportunities you’re looking for. Many employers use Applicant Tracking System (ATS) software for the first sift, so keywords boost your chance of being discovered.
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Clear formatting. Keep it simple, so that if ATS software is being used, it can read your CV.
Just because the market is demanding doesn’t mean securing your next role needs to be. There are clear trends emerging from AI-driven analytics to cloud-native skills to business focus.
By looking objectively at your unique skill set, your qualifications, your aspirations and your current achievements and working with a data and analytics recruitment partner who knows the industry inside out, we can support you in finding the next step.
