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The value of certifying your machine learning skills: A conversation with Dr. Fabian Stephany

Picture of David Arturo Amor Quiroz
The value of certifying your machine learning skills: A conversation with Dr. Fabian Stephany
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Arturo Amor, ML Engineer at Probabl and scikit-learn core maintainer, interviews Dr. Fabian Stephany from the University of Oxford about the value of ML skills and certifications

By Arturo Amor, ML Engineer at Probabl and scikit-learn core maintainer

We find ourselves in a period of profound uncertainty regarding the future of work. As software and AI redefine traditional workflows, policy makers and business executives alike are grappling with questions like: Which skills will remain relevant, and how do we build a workforce that is resilient to the next wave of disruption?

To find answers, we must move beyond speculation and look at the evidence. Dr. Fabian Stephany, an Assistant Professor in AI and Work at the University of Oxford, is at the forefront of this effort. Leading the multidisciplinary SkillScale Project, Dr. Stephany and his team use large-scale labor market data to provide empirical insights on how emerging technologies are reshaping work and the skills that are increasingly in demand.

In this post, I break down key findings from the SkillScale Project and share my conversation with Dr. Stephany about his latest insights on these questions.

What the data says about the value of ML skills

By analyzing millions of data points from online job vacancies and digital work platforms, Dr. Stephany and his team in the SkillScale Project have been digging into how the skill composition of professions is changing. I highlight three findings that stand out to me.

The value of complementarity: AI and ML skills pay off

In a 2024 research paper published in the prestigious Research Policy journal, the researchers found that the value of a skill depends on its complementarity; that is, by the number, diversity, and value of skills it can be combined with. By analyzing nearly 50,000 freelance projects, the researchers found that high-value skills like data analytics derive their worth from their complementarity and function as a force multiplier when paired with others.

The researchers also found that by having general AI-related skills, professionals can earn 21% more than their peers without such skills. This includes AI-adjacent roles where the professional uses AI to enhance their primary job (e.g. a marketer using AI for content generation or a project manager using AI for forecasting).

For data scientists, this means that your professional resilience is built not by hyper-specialization per se, but by developing a diverse set of interlocking skills that create strategic options for the future. For executives, this underscores a strategic shift in how to build human capital in your enterprise: your most resilient employees are those whose skill sets are diverse enough to offer strategic options for future reskilling.

Professionals with ML skills enjoy a 40% wage premium

In the same paper, the researchers identified a hierarchy of wage premiums based on the depth of a professional’s AI expertise. In particular, there is a significant wage premium for workers who have machine learning skills, who see a 40% increase in hourly wages. This specialized expertise represents the highest wage premium, followed by other types of AI skills such as deep learning (+27%) and natural language processing (+19%).

Crucially, this premium for machine learning skills is not confined to the tech sector. The researchers found that software and technical skills are often more valuable when applied in non-tech domains; for example, commanding ten times the value in Finance or Legal sectors compared to the Tech domain itself. This indicates that machine learning has become a key general-purpose skill, where the highest economic rewards go to those who can bridge the gap between technical execution and industry-specific application.

Certified ML skills increase likelihood of landing an interview invitation

In an experimental study involving over 1,700 recruiters across the US and UK, published in a 2026 working paper, Dr. Stephany and his team found how AI skills impact hiring decisions in graphic design, administration, and software engineering.

The researchers found that having ML and AI skills in your resume increases the likelihood of landing an interview invitation by up to 15%. Notably, AI skills can act as a powerful equalizer, capable of offsetting traditional labor market disadvantages related to age or lower formal education. In addition, verifiable certificates for machine learning and AI skills–particularly those issued by recognized universities or companies–act as a credible hiring signal.

 

A conversation with Dr. Fabian Stephany

I sat down with Dr. Fabian Stephany to better understand his latest insights on in-demand skills and how data scientists can upskill to remain competitive in the evolving labor market.

Arturo: Your research suggests that skills like data analysis and machine learning gain value when paired with others. For a data scientist today, what are the most underrated complementary skills that significantly boosts the market value of their technical expertise?

Dr. Fabian Stephany: We certainly see a strong premium for AI related skills such as data analysis, machine learning, and increasingly the application of AI agents in business workflows. But at the same time, our recent research shows that so called human or soft skills are becoming more valuable as AI spreads through the workplace.

The reason is quite straightforward. As AI tools become better at handling repetitive technical tasks such as cleaning datasets, refactoring code, or drafting reports and emails, this frees up cognitive bandwidth for workers to focus on areas where humans still have a comparative advantage. These include things like ethical judgment, communication, and teamwork.

Interestingly, when we look at occupations where AI adoption is particularly strong, we also observe rising demand for exactly these kinds of human capabilities. So for technical professionals such as data scientists, it is important to think beyond purely technical development. Technical expertise remains essential, but the professionals who will benefit most from AI are those who combine it with strong collaborative skills, the ability to translate technical insights into business decisions, and a sense of responsible and ethical deployment of these technologies.

In other words, the future value of technical expertise increasingly lies in how well it is embedded in human judgment and collaboration.

 

Arturo: In your 2026 working paper, you found that AI skills significantly increase interview invitations, even for non-technical roles like office assistants. Since claiming AI skills is becoming easier and more common, how can recruiters distinguish between a candidate who merely uses machine learning or AI tools and one who truly understands how to integrate them into professional workflows?

Dr. Fabian Stephany: This question essentially comes down to signaling. Today it has become relatively easy to claim AI expertise, sometimes simply because someone knows how to write prompts or use a particular tool. For recruiters, that makes it increasingly difficult to distinguish between buzzwords and genuine capability.

In our research we conducted a large online experiment with more than 1700 recruiters. Interestingly, we find that even self reported AI skills already increase the probability of being invited to an interview. But the effect becomes significantly stronger when these skills are accompanied by credible credentials.

Micro credentials, often short courses lasting one or two weeks offered by trusted industry providers or universities, greatly strengthen the signal. Candidates who list such certified AI skills receive substantially more interview invitations. This effect is particularly strong for applicants who might otherwise face disadvantages in the labor market, such as older workers or candidates with lower levels of formal education.

So while AI skills are increasingly common claims, credible certification from trusted institutions remains a powerful way to separate genuine capability from simple buzz.

 

Arturo: You’ve identified that employers are increasingly prioritizing practical AI skills over traditional degrees. Do you see a future where verifiable, hands-on certifications become the primary hiring signal for technical roles?

Dr. Fabian Stephany: What we observe right now is a shift toward skill based hiring. Employers increasingly focus on specific capabilities rather than relying solely on traditional degrees as signals.

However, this does not mean that degrees such as bachelor’s or master’s programs have lost their value. In many cases universities simply have not yet scaled up programs that focus specifically on applied AI skills. As a result employers currently rely more heavily on direct signals of skills because strong academic credentials in these areas are still relatively scarce.

Micro credentials, short targeted training programs, are filling this gap at the moment. They provide a fast and credible way to signal practical capabilities.

In the longer run, however, I expect universities to adapt. As more structured degree programs emerge around AI applications and computational skills, traditional academic credentials will continue to play an important role. The likely future is not the replacement of degrees but rather a hybrid system in which formal education and verifiable skill credentials complement one another.

 

Arturo: The finding that machine learning and AI skills are significantly more valuable in Finance or Legal than in Tech is interesting. Why does the translation of AI expertise into traditional industries command such a high premium?

Dr. Fabian Stephany: One explanation is the difference in technological maturity across sectors.

Many technical professions such as software engineering, machine learning engineering, or data science have already integrated forms of AI and advanced analytics for many years. Even before the recent wave of generative AI, these roles were already using machine learning methods and automation tools to optimize workflows. In other words, much of the productivity premium from these technologies has already been captured in the tech sector.

In contrast, sectors such as finance, legal services, or management are still in the earlier stages of adopting these technologies. Here the potential efficiency gains are often much larger. A lawyer, financial analyst, or manager who effectively integrates AI into their workflow may still see very substantial productivity improvements.

So the higher premium reflects the fact that AI adoption in these sectors is still catching up and therefore the marginal impact of AI expertise can be particularly large.

 

Arturo: Looking ahead to 2030, what is your prognosis for the sectors where skills in machine learning and AI will be the most impactful?

Dr. Fabian Stephany: Forecasts about the future of work tend to age notoriously badly, so I am cautious about making very precise predictions.

What we can say, however, is that AI has two distinct channels through which it creates value.

The first is efficiency gains, making existing processes faster, cheaper, and more reliable. In this dimension there is still enormous untapped potential, especially in small and medium sized enterprises where digital transformation is often still incomplete.

The second channel is genuine innovation, the creation of entirely new products, services, or scientific discoveries. This is much harder to predict.

To draw an analogy from the Industrial Revolution, early on we used steam power to improve existing processes such as mechanizing textile production. The real breakthrough came later when the steam engine was put on rails and created the railway system. That fundamentally transformed the economy.

With AI we are still largely in the phase of improving existing processes. The real transformative innovations are still ahead of us. One sector where this may become particularly visible is pharmaceuticals and biotechnology, where AI could dramatically accelerate the discovery of new drugs and treatments.

 

Learn more about the SkillScale Project

For a deeper dive into research findings from the SkillScale Project, explore the following resources:

📺 Watch

📚 Read

 

About Dr. Fabian Stephany

Fabian Stephany is an Assistant Professor in AI and Work at the Oxford Internet Institute (OII), University of Oxford and a Senior Research Fellow with the Institute for New Economic Thinking at the Oxford Martin School. He is also a Future of Work fellow at the Brussels-based think tank Bruegel, an inaugural fellow at Microsoft’s AI Economy Institute, and a research affiliate at the Humboldt Institute for Internet and Society in Berlin. Additionally, he currently serves as a member of the World Economic Forum’s Global Future Council for Human Capital Development.

At the OII, Fabian leads the SkillScale project, which views skills as a central lens through which to understand today’s labour market transitions. By examining how work quality, job growth, and labour market equitability and sustainability respond to technological change, the project investigates how AI skills are becoming increasingly pivotal for workers and employers alike. As part of his Microsoft fellowship, Fabian is currently exploring the role of AI skills in employability—particularly how working with generative AI enhances job prospects and addresses the gender gap between men and women.

Fabian is also a co-creator of the Online Labour Observatory–a digital data hub hosted in collaboration with the International Labour Organization that provides researchers, policymakers, journalists, and the public with insights into online platform work. His research has been published in leading academic journals, such as Research Policy and Scientific Reports, and has received media coverage in outlets around the world, including The Washington Post, The New York Times, The Telegraph, Nikkei Asia, Handelsblatt, and the Frankfurter Allgemeine Zeitung.

 

Learn more about Skolar by Probabl

The creators and maintainers of scikit-learn created the Inria MOOC “Machine learning in Python with scikit-learn,” as per the open source philosophy to empower everyone, everywhere, with free knowledge.

Since 2025, this mission is shared and actively supported by Probabl. The MOOC hasn't stopped evolving to adapt to new best practices, new functionalities in scikit-learn, and most importantly serve as preparation to get hands-on experience in a world where code generated by agents has to be validated by capable humans in the loop.

That’s where Skolar comes into play.

Learn more about Probabl’s free educational materials for machine learning with Python and our official Skolar certifications.

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