Behind The Data

Lightcast Taxonomies

When the right people have the right skills for the right jobs, then businesses, communities, educators, and individuals can all come together and create a job market that works for everyone. Understanding the labor market starts with identifying and clarifying what those jobs and skills are.

developer and list of skills

At Lightcast, we collect millions of labor market data points every day. In order to make use of them, we need to recognize how they connect. Our taxonomies are how: by organizing skills, occupations, and jobs into an understandable system, we can enable greater efficiency and optimization throughout the world of work and unlock new possibilities in the labor market.

An occupation describes the role a person performs, a title is what the individual's job is called (by employers or employees) and the skills are used to accomplish it.

list of skills

Jump to a specific section below:

Open Skills Taxonomy

Lightcast Occupation Taxonomy

Job Titles Taxonomy

Open Skills Taxonomy

32,000+ Skills

The entire labor market depends on skills—they're how you understand what a job demands and what a worker can supply. A skill can be anything from a formal certification to soft skills like collaboration and time management. Everyone's skill set includes, and every job requires, some combination of basic capabilities and more advanced specializations. Skills are how you articulate what you have and what you need in the clearest possible way.

On an individual level, you can come up with a list of skills on your own—you might list them on your resume. But that's simply not possible at an organizational level.

When a company's HR department needs to understand which skills are present among its workforce, when an education institution needs to standardize and articulate what skills it teaches its students, or when workforce developers needs to know what skills local workers will need in the future, the entire organization needs to be working from the same common language, and it needs to make sense in a broader labor market context.

Creating your own list of skills from scratch is time-consuming and labor-intensive. Many of the taxonomies that do exist are incomplete, are only useful within the organization or system itself, and lag behind actual market movement.

That's where the Lightcast Open Skills Taxonomy comes in, and it's available as a free, standalone API. We want everyone to speak the common language of skills, because that's how we enable a job market that works for everyone.

Lightcast tools collect real-world data from millions of job postings and online profiles every day, with a historical database of over 1 billion postings.

These novel sources provide insights about the labor market that are less apparent in traditional, survey-based data.

Our understanding of skills comes directly from how they're actually used in the labor market. And it happens in real time: Open Skills is updated every two weeks to reflect emerging skills requested in job postings—crucial for understanding new and fast-developing workforce trends like AI and the blockchain. Every new skill is recorded in our publicly-accessible changelog (between 30 and 50 are added with every update).

Lightcast’s data collection methodology fills in the gaps seen in traditional data-gathering, meaning that Open Skills is:

    Granular. Lightcast’s skills taxonomy is detailed enough to account for a variety of specific jobs and skills, allowing users to focus on distinct subsections of the labor market. Simultaneously, the taxonomy is organized to be accessible in aggregate, allowing for robust analysis. 

    Specific. Lightcast’s taxonomy accounts for both general skill areas as well as more distinct skills, allowing users to narrow in on precise aspects of a given career area. 

    Global. The taxonomy can be used across different businesses, industries, regions, or countries. The taxonomy can also be used in tandem with governmental labor market data, allowing for global comparisons.

    Responsive. The taxonomy is updated regularly, accounting for emerging skills and technologies.

Skills within the taxonomy are grouped by similarity, put into categories and subcategories, and also broken down into three classifications:

Common Skills are used in many different industries and occupations, including both personal attributes and learned skills. (e.g. "Communication" or "Microsoft Excel"). These include soft skills, human skills, and general competencies.

Specialized Skills are more specific—they’re primarily required within a subset of occupations or equip one to perform a specific task (like "NumPy" or "Hotel Management"). These are sometimes known as technical skills or hard skills.

Certifications are recognizable standards designated by industry or educational accreditation groups (including “Cosmetology License” or “Certified Cytotechnologist”).

Some new skills are identified by our AI software and internal tools, others through partner collaboration and research, but others are crowdsourced via our open submissions portal. Nobody knows your job better than you—so if you don't see a skill that should be in our library, suggest it here!

Whether they're submitted externally or identified by our AI tools, every skill in our library is vetted and organized by our team of dedicated taxonomists, who identify and articulate the relationships between related skills and between skills and jobs for a complete picture of the labor market. More than simply collecting data on the labor market, we refine, categorize, and draw meaning from it, so it's reliable and accessible right away.

Go Further With Skills

Using skills, employers and workers can find their best fits, while communities and educators can better equip the workforces of the future—by knowing exactly and clearly what a job requires and how to best meet those needs. Dive deeper into the Lightcast skills ecosystem.


The Lightcast Occupation Taxonomy

1,900+ Specialized Occupations

The Lightcast Occupation Taxonomy is how we organize occupations internally—and, like Open Skills, it comes from what we see in real-world postings and profiles.

The precision level of the LOT is intentionally designed to be both informative and accurate. It delves deeper into specifics compared to government systems like O*NET and SOC, without delving excessively into the intricacies of individual job titles. The LOT undergoes annual updates, striking a balance between stability and usefulness for longitudinal comparisons, while also promptly capturing newly emerging roles as they formalize within the economy.

The LOT uses a proprietary classification system of four different levels: career areas, occupation groups, occupations, and specialized occupations.

Career areas generally adhere to different industries. They provide an understanding of the defining skills that are necessary to enter any career within its scope.

Occupation groups are subsectors of career areas. They identify different functions of roles within a given career area.

Occupations are roles within an occupation group that point towards distinct goals within that group, usually demanding a well-defined skill set. These roles tend to align with national and government taxonomies, or individuals seeking to enter a workforce for the first time.

Specialized occupations are clusters of job titles and skills that define recognizable roles in the labor market. They are useful for analyzing specific skill requirements within a role, aligning with the needs of individuals seeking to advance their career.

As we move up in the hierarchy, subsections are exclusive to their associated category. In other words, each specialized occupation is unique to its occupation, each occupation is unique to its occupation group, and each occupation group is unique to its career area. This prevents duplicate or overlapping data when analyzing more than one occupation.

Using skills to understand occupations

In order to provide detailed information for students, jobseekers, workers, and other users of the taxonomy, skills need to be placed in the context of the careers in which they are relevant. Lightcast job posting and resume databases can be used to identify the skills typically required of a certain career and their relative importance to employers in the market.

The automated nature of this approach allows skill profiles to adapt in real time without expensive and complex profiling of roles by industrial organizational psychologists. The metadata described in the prior section develops multifaceted and detailed occupation profiles that highlight which skills are required for a role and why. Is a skill core to succeeding in a role, or is it a nice-to-have that may come with a salary boost? Is a skill versatile across a range of occupations, or have a distinct set of roles in which it is valuable? To better understand a skill’s role within a given occupation, each skill identified within a specific career is categorized within our occupational skills framework.

Necessary Skills are required for a specific job and are also relevant across other similar jobs. An employee needs these skills as building blocks to perform the more complex defining skills. In many cases, these are skills that involve managing the nuances and complexities of the workplace and as such are best learned through on the job training as part of an apprenticeship. For administrative assistants, necessary skills include record keeping and data entry. Repair and basic mathematics are both necessary skills for machinists.

For each specific role, the LOT also highlights the defining skills that represent the day-to-day tasks and responsibilities of the job. An employee needs these skills to qualify for and perform successfully in a certain role, and these skills are important to include in the initial classroom training provided to students to ensure their career success upon entering a job or internship. Examples of defining skills include accounting for bookkeepers, the ICD-10 coding system for medical coders, and entry-level programming languages such as Javascript, PHP, and HTML5 for web developers.

Distinguishing skills are sets of skills that allow job seekers to highlight their technical proficiency in a given role and to differentiate themselves from other candidates. These are skills that are less commonly required than defining skills and often represent the specific tools or digital skills in which jobseekers can specialize. In most cases, these types of tools and specializations are best taught in tandem with real-world practice for learners to crystallize their knowledge. Examples of distinguishing skills include specific types of marketing platforms such as Marketo and Google Adwords for marketing specialists, and creative design and video editing for graphic designers—it's possible to be successful in a job without these, but they're worth noting for advancement and recruiting.

See The Lightcast Occupation Taxonomy in the Analyst Platform

With Talent Analyst, Developer, and Analyst for education, you can tap into the most comprehensive insight available on labor market trends, including salary data, locations, and context from traditional data sources.

Lightcast Titles

75,000+ Titles

Lightcast Titles are an important part of the infrastructure supporting the function of the skills and occupation taxonomies. From the millions of raw titles collected from postings and profiles, our modeling team refines them down into a more standardized and accessible number (currently near 75,000).

The relationship between titles, occupations, and skills is fluid. Many titles map directly to an occupation in the LOT, but not all: the title of "Air Traffic Controller" would correspond directly to the "Traffic Controller" occupation, but that relationship is not fixed in the way the four levels of the occupation taxonomy are fixed to one another.

Open Titles allows organizations to create a consistent standard to organize their core jobs data, to create a connection to external labor market data, and connect to useful taxonomies like LOT and Open Skills. It is a translation layer from what organizations call their jobs to useful external context so they can benchmark, compare, and analyze their data outside of their four walls. It creates universal meaning versus an inward-looking limited view.

The purpose of the Titles taxonomy is not to identify the skills associated with the occupation of fast food worker; the others are more effective in defining those relationships. Instead, the Titles taxonomy is used to recognize that a "sandwich artist" being hired at Subway is, for all intents and purposes associated with labor market data analysis, a fast food worker.

Go Deeper into the Data

We've put a lot of thought and care into making sure our data serves the entire labor market. If you want to hear more, we're here to help.