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Job Postings Data: Best Datasets on Rocks.Gold

Andrei Klimovich
June 29, 2021

Using various types of job postings data, you can answer multiple questions, like

  • Status of the labor market
  • Level of current market economic activity
  • Build a better vision of the thriving and shrinking industries

However, all of these questions can be answered only based on the quality and quantity of data you can get. 

If the data sample is too small or too dirty (multiple duplicates), you can't trust your results.

So, let's have a look at how the best dataset should look like

Best Job Postings Datasets

First of all, we need to decide on the context of Job Data Usage to talk about the best datasets.

There are a few most common job posting data buyers and/or users -

  1. Job Boards
  2. Research Agencies
  3. In-house Data Science Team
  4. Recruitment Agency
  5. Sales/Marketing Teams

All of them will have very different needs regarding data volume, quality, and delivery technology. 

Job boards may need automatic plug-in in the system, real-time data gathering capabilities, strict deduplication process. In many cases, the uniqueness of job data will be higher in priority compared to volume.

Research agencies may need big datasets with a strict split from what industries that hiring request came from. Also, they may have a project to make a historical analysis of a particular set of companies. In that case, you will need a clear enrichment and matching mechanism to connect hiring data with the raw version of "Company Name" in many cases to an actual company ID. In terms of data quality and quantity, it is on the higher level of need based on company reputation, maturity, and team seniority. When a new research project starts, a data analyst job posting is widespread for many companies to start hiring for. The same logic goes to data entry job posting.

In-house Data Science Team may have similar requests as a research agency, but with more niche business needs. Compared to the agency with changing needs in terms of the final dataset, the in-house team, in many cases, will be more structured in that sense. In terms of quality and quantity, frequently project-based work from private businesses is not on top-level and requires general data coverage.

Recruitment agencies will need clear separation of hiring data from recruitment and staffing agencies. Also, the ability to check their own set of current customers is essential. Finally, multiple firmographics and hiring filtering capabilities are excellent to have!

Sales/Marketing Teams will have a strict number of data points they need to have to make data work -

  1. Great firmographic profile like company website, company industry (not recruitment/staffing agency), company size
  2. Clear hiring trigger - a strict set of job names, keywords in the job description, or group of skills
  3. Frequency or freshness of a catalyst - they need to understand if target hiring is cyclical, can't be closed, or this post appeared just right away, and it is the best time to connect with a company. 

As a result, if the company provides analytical data service, they will review data posting jobs.

So what dataset can we consider as the best for all mentioned buyer personas? 

  1. Rich filtering capabilities among hiring data - What to get data with Python Developer as title and Machine Learning in a job description? No Problem!
  2. Rich company profiles and matching algorithms - Best dataset should not have 20 million job posts and just 10 thousand companies.
  3. Strict deduplication mechanism - there are a lot of copies of data from multiple job boards. Data vendors need to deduplicate the records to avoid the case that 20 people company is hiring for 50 developers, etc.
  4. Real-time data flow - if you want to provide value for job boards and sales/marketing teams, you need to send and share data soon after collection and processing. There is no significant interest in historical datasets but more in timely delivery.
  5. Low volume of empty fields - You have 5% of open job descriptions? Data is invalid cause you don't have an essential part. No company name/website? Again, if the information is low quality, we can't deduplicate job postings or match them with the existing account. In general, any raw data with an empty field used in analytics should be dropped despite all other available data points.

Where can you look for job posting activity datasets? Every provider has a different offer - 

  1. Rocks & Gold
  • Mainly IT job posts
  • Complete company profiles or raw data
  • Big focus on matching job data with company and filtering duplicate postings
  • Rich filtering capabilities via UI or on request with SQL
  • Over 35 job boards from LinkedIn to WeWorkRemotely
  1. Coresignal
  • All types of job data
  • Raw data
  • Big focus on delivering big historical datasets with active job posts highlights
  • No filtering capabilities, data dump purchase method
  • Most focus on open to Google Index LinkedIn job posts
  1. LinkUp
  • All types of job data
  • Complete company profile based on website domain
  • Focus on data collection from original company domains only (as a result, only 50k active company profiles)
  • No info about filtering capabilities
  • A vast historical dataset on many companies collected directly from their website
  1. Zyte
  • Real-time custom crawling service
  • All types of data
  • Flexible terms of data collection
  • No historical data, you select what to crawl and start data collection

Features of Job Postings Data

In the context of a job-posting-matching system, the data are a good source of structured information on active job postings. They are suitable for determining who might or might not apply for an open job based on answering a specific set of questions.

Data about online job postings can be helpful for the following purposes:

A) Match applicants to employers to determine who is hiring

B) Help employers get in touch with applicants to learn their level of interest

C) Identify people who shouldn't be placed in a job if they don't fit a specific set of requirements.

D) Find companies in buying mode for particular service or expertise based on job posting data

The key benefits of using this data are that it provides a lot of information about who is posting positions at any given moment, how long they post positions for, and who they post them for.

One thing to note is that this data is not as complete as what the HR office can provide.

Job Description can show what skills and tech company possess currently and what expertise it is lacking.

Job Name can show a generalized summary of job description datapoints and size down the search.

Job Compensation can show company maturity, need, and health. Many companies with no clear goals and vision about the role put many requests in the job description, keeping compensation as low as possible.

Job Seniority can be used as an additional filter or a comparison between job name/description and a vision about role seniority. 

Job Remote Policy can be used as another filter and present company working culture/policy. You can find parameters as Only In-house, Remote Only, Open to remote, Travel required, and some other points.

Benefits of Job Postings Data

There are several benefits of using active postings data based on user profile - Business Entity or Consumer (Job Seeker).

As for job seekers, there is no denying that finding jobs nowadays is more important than ever before. Finding a job and having a stable job that you look forward to each day are the keys to happiness in today's world. Many people go for so-called 'recruitment sites' to search for a company to work for and then apply online. This is the way most job searchers still search for jobs as they've seen others trying their luck on these websites (and websites like these).

For them, sites with deep filtering capabilities give the best benefits to find as fast as a possible desired list of hiring requests and apply to them.

As so business goals and hiring companies, in particular, different actors have different benefits. 

So, hiring managers out there are aware of the 'active job postings' and 'job ads' that they see these days and if they have been following any 'recruitment campaign' using job posting as the tool of recruitment. They have a pretty good idea of what the search for a job entails. That's why they use these data to find out a lot about the job seekers and how well they can adapt to any changes that the company will bring.

Hiring companies can check what skills are in significant demand on the market, train their internal employees, or double-check on where the market is going.

As such analytical business goals, companies can get a pretty inexpensive dataset with a current industry snapshot. For example, compared to interviewing 100 CTOs of Fortune 500 companies in a particular industry on their future goals and initiatives, the researcher can buy a specific hiring dataset to get an external view of the situation. 

As for growing business decisions, based on service and product offering, many marketing and sales teams can have better targeting in their outreach and digital ads campaigns using the latest job postings data as intent and/or sales triggers.

Job Postings Data Pricing

Source of Job Analytics is not that expensive to check labor market conditions or other business goals

There are a few options on the market in terms of data pricing, but mostly all of the offerings will be fixed monthly fees with some number of records to use/export/consume.

Let's compare a few - 

  1. Rocks & Gold - pricing has two options- raw data (collected directly from job boards with no processing) and complete data. The difference is 10 times bigger between these two offerings. Despite what plan it is, any purchase starts from $10.000 with a trial/test period of $500.
  2. LinkUp - pricing is based on consumed data volume. It starts from a $1.000 per month commitment
  3. Coresignal - proposing to buy complete raw historical data from LinkedIn from $5.000 and get weekly updates during 1 quarter for the same price.
  4. Jobspikr - pricing is fixed, monthly/annual, with different amounts of export credits and data quality. Starts as low as 99$ per month.

FAQ

How are job postings data collected?

Most data providers collect data via web scraping job postings. They go on job boards daily using custom crawling bots. Also, some vendors develop in-house parsing farms to collect data directly from company websites. Finally, Job posting data can be collected from search index sites like Google.

Why are some job postings confidential?

Based on the Privacy Policy and Terms of Usage on several websites, the collection of postings can be confidential for non-registered users. Also, let's take a more global view on the problem. Some industries have a secret policy and needs in the security industry, so hiring is done only via trusted recruitment agencies with the anonymity of the hiring company.

What should be included in a job posting?

Job post should contain a clear list of required skills, job compensation, level of seniority needed, list of perks, and benefits for employees. Based on the shortage of talent, the different job postings will have another description. The more competitive the position (many candidates available on the market), the less clear and appealing the hiring post will be. Also, it might depend on the company HR talent who was writing a copy. Based on current trends, compensation and a clear list of duties will be the most critical parts of a resume. Company culture, work conditions, and opportunities will go next in importance.

Job Postings Data: Best Datasets on Rocks.Gold

Andrei Klimovich
June 29, 2021

Using various types of job postings data, you can answer multiple questions, like

  • Status of the labor market
  • Level of current market economic activity
  • Build a better vision of the thriving and shrinking industries

However, all of these questions can be answered only based on the quality and quantity of data you can get. 

If the data sample is too small or too dirty (multiple duplicates), you can't trust your results.

So, let's have a look at how the best dataset should look like

Best Job Postings Datasets

First of all, we need to decide on the context of Job Data Usage to talk about the best datasets.

There are a few most common job posting data buyers and/or users -

  1. Job Boards
  2. Research Agencies
  3. In-house Data Science Team
  4. Recruitment Agency
  5. Sales/Marketing Teams

All of them will have very different needs regarding data volume, quality, and delivery technology. 

Job boards may need automatic plug-in in the system, real-time data gathering capabilities, strict deduplication process. In many cases, the uniqueness of job data will be higher in priority compared to volume.

Research agencies may need big datasets with a strict split from what industries that hiring request came from. Also, they may have a project to make a historical analysis of a particular set of companies. In that case, you will need a clear enrichment and matching mechanism to connect hiring data with the raw version of "Company Name" in many cases to an actual company ID. In terms of data quality and quantity, it is on the higher level of need based on company reputation, maturity, and team seniority. When a new research project starts, a data analyst job posting is widespread for many companies to start hiring for. The same logic goes to data entry job posting.

In-house Data Science Team may have similar requests as a research agency, but with more niche business needs. Compared to the agency with changing needs in terms of the final dataset, the in-house team, in many cases, will be more structured in that sense. In terms of quality and quantity, frequently project-based work from private businesses is not on top-level and requires general data coverage.

Recruitment agencies will need clear separation of hiring data from recruitment and staffing agencies. Also, the ability to check their own set of current customers is essential. Finally, multiple firmographics and hiring filtering capabilities are excellent to have!

Sales/Marketing Teams will have a strict number of data points they need to have to make data work -

  1. Great firmographic profile like company website, company industry (not recruitment/staffing agency), company size
  2. Clear hiring trigger - a strict set of job names, keywords in the job description, or group of skills
  3. Frequency or freshness of a catalyst - they need to understand if target hiring is cyclical, can't be closed, or this post appeared just right away, and it is the best time to connect with a company. 

As a result, if the company provides analytical data service, they will review data posting jobs.

So what dataset can we consider as the best for all mentioned buyer personas? 

  1. Rich filtering capabilities among hiring data - What to get data with Python Developer as title and Machine Learning in a job description? No Problem!
  2. Rich company profiles and matching algorithms - Best dataset should not have 20 million job posts and just 10 thousand companies.
  3. Strict deduplication mechanism - there are a lot of copies of data from multiple job boards. Data vendors need to deduplicate the records to avoid the case that 20 people company is hiring for 50 developers, etc.
  4. Real-time data flow - if you want to provide value for job boards and sales/marketing teams, you need to send and share data soon after collection and processing. There is no significant interest in historical datasets but more in timely delivery.
  5. Low volume of empty fields - You have 5% of open job descriptions? Data is invalid cause you don't have an essential part. No company name/website? Again, if the information is low quality, we can't deduplicate job postings or match them with the existing account. In general, any raw data with an empty field used in analytics should be dropped despite all other available data points.

Where can you look for job posting activity datasets? Every provider has a different offer - 

  1. Rocks & Gold
  • Mainly IT job posts
  • Complete company profiles or raw data
  • Big focus on matching job data with company and filtering duplicate postings
  • Rich filtering capabilities via UI or on request with SQL
  • Over 35 job boards from LinkedIn to WeWorkRemotely
  1. Coresignal
  • All types of job data
  • Raw data
  • Big focus on delivering big historical datasets with active job posts highlights
  • No filtering capabilities, data dump purchase method
  • Most focus on open to Google Index LinkedIn job posts
  1. LinkUp
  • All types of job data
  • Complete company profile based on website domain
  • Focus on data collection from original company domains only (as a result, only 50k active company profiles)
  • No info about filtering capabilities
  • A vast historical dataset on many companies collected directly from their website
  1. Zyte
  • Real-time custom crawling service
  • All types of data
  • Flexible terms of data collection
  • No historical data, you select what to crawl and start data collection

Features of Job Postings Data

In the context of a job-posting-matching system, the data are a good source of structured information on active job postings. They are suitable for determining who might or might not apply for an open job based on answering a specific set of questions.

Data about online job postings can be helpful for the following purposes:

A) Match applicants to employers to determine who is hiring

B) Help employers get in touch with applicants to learn their level of interest

C) Identify people who shouldn't be placed in a job if they don't fit a specific set of requirements.

D) Find companies in buying mode for particular service or expertise based on job posting data

The key benefits of using this data are that it provides a lot of information about who is posting positions at any given moment, how long they post positions for, and who they post them for.

One thing to note is that this data is not as complete as what the HR office can provide.

Job Description can show what skills and tech company possess currently and what expertise it is lacking.

Job Name can show a generalized summary of job description datapoints and size down the search.

Job Compensation can show company maturity, need, and health. Many companies with no clear goals and vision about the role put many requests in the job description, keeping compensation as low as possible.

Job Seniority can be used as an additional filter or a comparison between job name/description and a vision about role seniority. 

Job Remote Policy can be used as another filter and present company working culture/policy. You can find parameters as Only In-house, Remote Only, Open to remote, Travel required, and some other points.

Benefits of Job Postings Data

There are several benefits of using active postings data based on user profile - Business Entity or Consumer (Job Seeker).

As for job seekers, there is no denying that finding jobs nowadays is more important than ever before. Finding a job and having a stable job that you look forward to each day are the keys to happiness in today's world. Many people go for so-called 'recruitment sites' to search for a company to work for and then apply online. This is the way most job searchers still search for jobs as they've seen others trying their luck on these websites (and websites like these).

For them, sites with deep filtering capabilities give the best benefits to find as fast as a possible desired list of hiring requests and apply to them.

As so business goals and hiring companies, in particular, different actors have different benefits. 

So, hiring managers out there are aware of the 'active job postings' and 'job ads' that they see these days and if they have been following any 'recruitment campaign' using job posting as the tool of recruitment. They have a pretty good idea of what the search for a job entails. That's why they use these data to find out a lot about the job seekers and how well they can adapt to any changes that the company will bring.

Hiring companies can check what skills are in significant demand on the market, train their internal employees, or double-check on where the market is going.

As such analytical business goals, companies can get a pretty inexpensive dataset with a current industry snapshot. For example, compared to interviewing 100 CTOs of Fortune 500 companies in a particular industry on their future goals and initiatives, the researcher can buy a specific hiring dataset to get an external view of the situation. 

As for growing business decisions, based on service and product offering, many marketing and sales teams can have better targeting in their outreach and digital ads campaigns using the latest job postings data as intent and/or sales triggers.

Job Postings Data Pricing

Source of Job Analytics is not that expensive to check labor market conditions or other business goals

There are a few options on the market in terms of data pricing, but mostly all of the offerings will be fixed monthly fees with some number of records to use/export/consume.

Let's compare a few - 

  1. Rocks & Gold - pricing has two options- raw data (collected directly from job boards with no processing) and complete data. The difference is 10 times bigger between these two offerings. Despite what plan it is, any purchase starts from $10.000 with a trial/test period of $500.
  2. LinkUp - pricing is based on consumed data volume. It starts from a $1.000 per month commitment
  3. Coresignal - proposing to buy complete raw historical data from LinkedIn from $5.000 and get weekly updates during 1 quarter for the same price.
  4. Jobspikr - pricing is fixed, monthly/annual, with different amounts of export credits and data quality. Starts as low as 99$ per month.

FAQ

How are job postings data collected?

Most data providers collect data via web scraping job postings. They go on job boards daily using custom crawling bots. Also, some vendors develop in-house parsing farms to collect data directly from company websites. Finally, Job posting data can be collected from search index sites like Google.

Why are some job postings confidential?

Based on the Privacy Policy and Terms of Usage on several websites, the collection of postings can be confidential for non-registered users. Also, let's take a more global view on the problem. Some industries have a secret policy and needs in the security industry, so hiring is done only via trusted recruitment agencies with the anonymity of the hiring company.

What should be included in a job posting?

Job post should contain a clear list of required skills, job compensation, level of seniority needed, list of perks, and benefits for employees. Based on the shortage of talent, the different job postings will have another description. The more competitive the position (many candidates available on the market), the less clear and appealing the hiring post will be. Also, it might depend on the company HR talent who was writing a copy. Based on current trends, compensation and a clear list of duties will be the most critical parts of a resume. Company culture, work conditions, and opportunities will go next in importance.

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Most data providers collect data via web scraping job postings. They go on job boards daily using custom crawling bots. Also, some vendors develop in-house parsing farms to collect data directly from company websites. Finally, Job posting data can be collected from search index sites like Google.

Why are some job postings confidential?

Based on the Privacy Policy and Terms of Usage on several websites, the collection of postings can be confidential for non-registered users. Also, let's take a more global view on the problem. Some industries have a secret policy and needs in the security industry, so hiring is done only via trusted recruitment agencies with the anonymity of the hiring company.

What should be included in a job posting?

Job post should contain a clear list of required skills, job compensation, level of seniority needed, list of perks, and benefits for employees. Based on the shortage of talent, the different job postings will have another description. The more competitive the position (many candidates available on the market), the less clear and appealing the hiring post will be. Also, it might depend on the company HR talent who was writing a copy. Based on current trends, compensation and a clear list of duties will be the most critical parts of a resume. Company culture, work conditions, and opportunities will go next in importance.