A holistic approach to salaries, learning & development, and recruiting

A skills-based organization prioritizes the abilities and competencies of its workforce over traditional metrics such as tenure, job title, or education level. In these organizations, the emphasis is on identifying, nurturing, and deploying skills to align with their strategic goals.

Let’s start off with an example: Continental, a leading German automotive manufacturer, implemented the “Big Data for Best Fit” project to align its workforce’s skills with future business needs using machine learning and strategic planning. This approach enabled the company to dynamically adapt to market changes by evaluating and planning for key skills and prioritizing abilities over traditional metrics like tenure or titles.

This approach allows for a more dynamic and flexible response to market changes and organizational needs, fostering an environment where continuous learning and adaptability are at the forefront.

Skill-based organizations are usually characterized by:

• Dynamic workforce allocation. Skill-based organizations can match the right skills to the right tasks, regardless of the employee’s formal position. This fluidity allows for a more responsive and adaptable organizational structure.
• Continuous learning and development. There is a strong emphasis on upskilling and reskilling employees to meet the evolving demands of the market. Learning opportunities are tailored to individual skill gaps and career aspirations, promoting a culture of lifelong learning.
• Performance metrics focused on skills. Evaluation and advancement consider skill acquisition and application in addition to traditional performance metrics. This shift encourages a growth mindset and incentivizes skill development.
• Inclusive hiring practices. Skill-based organizations foster a more diverse and inclusive workforce by focusing on skills and potential rather than credentials. This approach helps break down traditional barriers to employment and leverages a wider talent pool.

Companies often say they have one “data scientist” job in their architecture. The architecture defines salaries offered to new hires and those who develop and progress within the company.

Therefore, we thought it was time to reverse engineer this role and see how skill value determines market salaries. The result is the skills pyramid, which structures job value into three levels. And we will explain it with an example of a data scientist’s skills.

For example:

• Basic ability. It emphasizes the fundamental layer of ability that underpins more complex skills like ‘I can program.’
• Concrete skillset. It’s refined to rusty vs. modern and highlights the range of skills from time-honored or outdated methodologies to cutting-edge, modern techniques.
• Skills proficiency within the skillset. It evolves into a mastery level within the skills spectrum (beginner vs. expert) to accommodate the continuous nature of skill development, from initial learning stages to complete expertise.
• Focus & application area of skills. It underscores that the value and compensation for skills are greatly influenced by how effectively they are applied in real-world scenarios, industries, or projects.

How much will the market pay for someone who can ‘program code’?

Here, the base skill-knowledge level determines the data scientist’s programming language knowledge and the market value for this language. For example,

• Legacy and rusty languages like VBA have lower skill value than thriving languages like Java or Python.
• Modern languages with a well-established developer community command higher skill value.

When exploring different fields, one can consider the value of programming languages such as Java or Python, as shown in the figure above. About spoken languages, questions arise about the comparative value of English and German or other languages. Similarly, in the area of tool knowledge, the capabilities of Microsoft Excel and Adobe Photoshop can be compared. The importance of interaction styles, including basic versus cross-cultural communication and openness, also comes into play.

How much more is ‘expert’ knowledge rewarded than ‘beginner’ knowledge for any skill like Python?

Here, we break down the skill into different proficiency levels and see how the skill value grows as the software developer’s experience grows. We can see nearly a 21% pay rise regarding skill value growth based on experience gained.

How much does the skills application area decide, and how much am I being paid?

Expertise in a specific skill area makes you a ‘hard-to-lose’ employee or a ‘hard-to-find’ candidate. And thus, the skill value for expertise increases its skill value. For example, IBM has implemented a progressive shift in its compensation strategy to value expertise in specific skill areas. Moving away from a performance-only model, they adopted a multifactorial approach. This change reflects the recognition that skills change over time, with a “half-life” for any acquired skill of around five years. This strategy is consistent with the evolution of technology and the need for continuous upskilling and retraining of the workforce.

Here are some main findings we’d like to share with you. The value across the pyramid from base skill to expertise level shows the following and answers to the previous questions:

• How much does the application area of a skill impact the value of the skill? You can earn up to 31% and more.
• How much does experience/proficiency matter? You can earn up to 21% and more.
• How much do the skills I know within programming matter in terms of value? The answer is up to 53% (41k for VBA vs 66k for Python).

The combination of skills and experience results in vast differences ranging from 41k (VBA basic programmer) to 135k (expert Python developer with MlOps knowledge).

Knowing or having programming skills can sometimes get you the highest salary. Still, the most significant impact on your maximum salary depends on which programming language you specialize in. You can see from the pyramid that Python and Java earn considerably more than VBA. The following reasons can cause this:

• Developer community support / Language power. Python Package Index (PyPI) hosts over 300k packages.
• Migration from legacy systems. With companies moving towards improved performance and maintainability, enhanced security, and access to a broader ecosystem, migration towards modern languages like Python, JavaScript, and Golang is on the rise.
• Cross-platform compatibility. Many modern languages are designed to be cross-platform, which means applications written in these languages can run on multiple operating systems with minimal modifications. It can be a significant advantage for companies targeting a broad user base, increasing their value.
• Future-proofing skills. Companies often want to ensure that their software systems remain relevant and adaptable in the long term. Migrating to a modern language can future-proof their applications by taking advantage of technological advancements.

Just like we saw that proficiency and expertise impact salaries, this also differs from country to country. It can also differ from region to region within a country.

Most interesting is how skill value rises between pyramid levels in different countries.

For a clearer perspective, consider this: mastering a particular programming language in the UK can lead to a substantial 53% increase in value. Conversely, the same achievement in India translates to a modest 14% increase. It shows that mere possession of programming skills, regardless of the language, doesn’t substantially impact earnings in India. This likely reflects the region’s abundant talent pool.

However, as you grow in experience and expertise, we see salary growth of up to 83% compared to 21% in the UK. It shows that as you grow in experience, the market value will also increase significantly.

• Evaluate the value of candidate skills to avoid overpaying or underpaying rather than the roles or past experiences.
• Implement policies and procedures on handling compensation for skills within the organization’s organizational level, like common roles or across hierarchies.
• Provide retention of top talent by understanding and offering appropriate compensation based on experience and expertise.

• Plan for skills rather than for job families or roles – planning tools nowadays allow you to cope with the complexity that comes along when planning on a skills level. This will add more value to your organization and make connecting business drivers with workforce strategy easier.
• Improve decision-making about building or buying strategies. Decide which skills you should build and which ones you should acquire externally based on their value. There are huge savings potentials if your organization relies on a contingent workforce.
• Evaluate how valuable your workforce assets are and quantify the return of the processes across your employee life-cycle (e.g., how much skills value have my learning & development investments generated).

Skill values can also point to certain trend areas in which businesses invest.

• For example, the high value of NLP in the pyramid shows that with the rise of ChatGPT and other language-based AI tools, companies are now ready to spend more in this area of research.
• Another example is MLOps, a paradigm that organizations are adopting. It can also be seen in the skill value.
• With this, you can understand the latest trends, hire upskilled internal talents to stay abreast of the market, and incorporate this into your L&D and recruiting processes.

• Track how L&D investments translate into skill value and measure ROI to make more value-focused decisions.
• Ensure that skills are developed with high value. To stick with the example above, there is a much smaller ROI for building VBA skills within the workforce than for building MLOps expertise.

• Improve the flexibility of a job-grade-based system with a skill-based component. It will enable you to reward specialized skills, increase retention, foster future skills learning, and attract talented talents.
• Plan and integrate L&D options for specializations within the organization. It would help you upskill your talents in the latest market trends and solutions.
• It will be challenging if you are a tariff-based organization, as tariffs don’t compensate for skills yet.