Benchmarking Job Levels

Most companies use market data to determine their employees' compensation. It is essential to correctly assign and tag the role levels and role families at your company, as this enables us to match your roles to other companies' roles for accurate calibration. For more information about Pave's job levels, please also take a look at this article. 

Role Family - this is the function a role is a part of, often discernible by the department or team this role falls in at your company.

Role Level - this is how senior, experienced, and the scope of responsibility that the role covers.

Role Track - this is the determination of whether the role is a Professional, Manager, or Support

Pave’s leveling schema is designed for companies of all sizes. The schema is easily matched to Radford and Advanced HR so that the market data can stay current and accurate to what you might already be using. You can learn more information about our Leveling Schema in this sheet. 

Titles describe both what a role is responsible for and how senior it is. Just like how you’ve decided to name your conference rooms, titling structure is unique to a company’s culture. This is why we use other sources of qualitative and quantitative data outside of just job titles and departments to ensure the benchmarking data is constructed accurately.

Titles are an important indication of the work and seniority of a role, but it can also over complicate things for smaller companies (under 150 employees). Based on the research we’ve done it’s common for startups to implement a titling structure between 100-150 employees. For those of us who’ve worked in small and growing startups, we know that the work and roles can span multiple levels and have a very wide range of responsibilities. Then, as the company grows, the roles become more specific and specialized. At startups, roles and responsibilities are constantly changing, so it’s easy for titles to become misleading or even inaccurate. This is why it’s important to define levels and career ladders sooner rather than later.

Pave uses all sorts of existing data within your systems to match and label the existing roles at your company. For example:

We know that this information isn’t the same at every company, or in every industry, so we use a mixture of machine learning and human validation to ensure that roles are classified accurately.