Scrape Job Postings From Internal And External Sources

Most recruitment agencies and online job boards scrape job postings from multiple sources, aggregate the job data, and provide candidates with a broader list of opportunities to explore. The internal job postings include career pages of the company website. While the external job postings include any other source apart from the company — job boards, job listing sites, and more.

The Difference Between Internal and External Job Postings

Apart from the fundamental difference of the job source, there lie more differences between internal and external job postings.

Scrape Job Postings From Company Websites

As for company job posts, the variations should be far too less but that generally, is not the case. Some well-established companies have proper career pages with different sections like Technology, Product, Marketing, and Human Resources. Then, specific jobs are listed under these sections having the job role, description, location, good to have, salary, benefits, and more. If the company is a multinational entity and hires across different regions. Then you might need to select a specific country or city before being able to view the job listings. However, these are the best-case scenarios where the data is presented just like it is in job websites; the only difference being that all the job posts are from a single company.

On the other end of the spectrum are startups and other companies that do not hire regularly. These companies usually do not have a specific career page or even if they do. The page itself is not well maintained. They usually ask candidates to send their resumes to an HR email address to apply for possible open positions. Others have a generic job description and the type of work undertaken for every department. But do not have different job descriptions for specific job titles. This makes it difficult to gather information about job openings for these companies.

How to Scrape Internal and  External Job Data?

When it comes to scraping job posts, external job listings are much easier to tackle. Let me explain why. When you train your scraping engine to scrape data points from a single job post from a job board. You can use the same code to scrape hundreds or even thousands of other job posts. All this on the same job board. What you will need to handle programmatically is how to parse through the webpages. You can automatically go from one job post to another. Scraping web pages in parallel to reduce the time taken for the process can be another challenge.

While tackling these common issues related to scraping job listings from external websites. You also need to make sure that the site does not block you due to multiple requests coming from the same IP together. This can be handled through IP rotation and VPNs.Another benefit of scraping from external job websites is that if you want to scrape jobs using a specific filter, or keywords, you can do that by using the filters present on the webpages.  

As for different company websites. You will need to create a long list of companies (you can start with the Fortune 500). Listing the company careers pages and manually checking if they have a separate career or job listing page may be a laborious task. Scraping job postings from every single webpage may take even more time.

The reason behind this is that you will need to analyze the webpages in every single company website that you need to scrape before you can get job listings from each. Also, every website will probably end up giving you a few job posts only. The time spent analyzing each website will not seem to be very rewarding. But doing this is important since job applicants will want to view job posts from top companies.

How To Go About Scraping Job Data?

If you are planning to analyze and scrape both internal and external job data, you must have data analysts and software engineers with web-scraping experience. Starting from scratch will not be easy, but if you have an experienced team, you can still go for it. But if you need a quick solution and do not want to take up the hassle of building and maintaining the solution and taking care of the cloud infrastructure required. The best thing for you would be a DaaS (Data as a Service) solution specialist like PromptCloud.

Our team at PromptCloud has come up with an automated job discovery solution called JobsPikr that makes it much easier for job agencies to scrape job postings from multiple sources. You can enhance your hiring platform using data from JobsPikr to benefit both your clients and applicants, and generate a higher revenue stream.

Data Scientist: The Most Popular Job of the 21st Century

Data Scientist touted to be “The Sexiest Job of the 21st Century” in 2012 by The Harvard Business Review, a little more than 10 years after the term coined in 2001! Come 2020, and we are facing a shortage of anywhere between 200,000-350,000 data scientists all over the world.

So, what exactly is a data scientist? What does he/she do? Do they have to be a real scientist with a Ph.D. degree? Well, let’s find out.

A data-scientist does not require formal college degree, rather the right set of skillsets. Based on the size of a company he/she may also take up the responsibilities of a data engineer, typically here are some of the must-haves:

  1. Coding and scripting knowledge
  2. A deep understanding of fundamental and advanced statistics
  3. Calculus and algebra
  4. Machine learning
  5. Data wrangling
  6. Data visualization and communication

Who Is A Data Scientist?

A company may require managers to plan products. Engineers to build them and marketing and advertising personnel to spread the word. You may also have other departments like operation and logistics based on the requirement. A data science team or a Data Scientist is not there to run your business. So why are they important? The answer is, to grow your business.

In the earlier days, every department used to conduct its work and, in the process, generate a lot of data. Unfortunately, this data was never passed on to other departments to take a look into. For example, say the marketing department receives negative feedback in its survey sheet regarding deliveries (which is not directly linked to the product). This information needs to reach the operations team but does not because of the lack of a data flow process.

A Data Scientist, if present, would analyze the structured and unstructured data produced by different departments of your company.  They then enhance this data using information gathered from external sources and then build data models and make predictions and find hidden trends. These would then communicated to the entire company so that each department can use the information relevant to them to drive the product ahead.

Data Scientist

Fig: Percentage of time devoted to different functionalities by a data scientist.

Why Do You Need A Data Scientist?

Companies fetch data from multiple sources today. Data can be in the form of internal data generated by different teams based on their interactions with users. It can be system data log files generated by the system that captures the activity of different users on your website. It can also be external datacompetitor pricing data gathered through web-scraping. Data can also capture from various devices (using the Internet of Things) and appliances. Today almost anything you buy. Be it a fridge or a watch connected to the internet (read IoT). All this data needs cleaning, analyzing, and conversion to a format understood and absorbed by the business team. All of this taken care of by the Data Scientist. They not only take care of all the data sources at hand and communicate findings to all but also look into other sources to boost business and increase productivity.

There was a time when data meant excel-sheets. It’s consumed easily by the business team easily. Today, data has boomed and a very small percentage of that data is in a structured tabular format. Big data exists in semi-structured formats like JSON on XML and even in unstructured formats such as images, pdfs, videos, audio recordings, or plain text. While “plain text” may seem to be a good data source, breaking it into pieces and extracting information from it is a subject in itself, called NLP or natural language processing.

Understanding The Role Of A Data Scientist

Data science is the understanding of what a text means using code. Its knowledge is essential for data scientists who work on textual data. One of the common sources of data with all formats is social media texts, images, videos, and more, all sitting together. With the new and varied data types that are not analyzed manually just by looking at the data. The business team is in a fix. The Data Scientist enables the business team to understand the data by converting it into a simpler format, breaking it down to understandable bits.

A pile of data is of no use unless worked upon. A Data Scientist uses the various tools at his/her disposal to understand the data first, and then uses data wrangling techniques to convert it to a workable format. Using this converted data, they usually create predictive-models. These models help the business team weigh different metrics against each other and find the optimum way to maximize business gains.

The Demand For Data Scientists?

The average base salary for a Data Scientist is $113000/year. And yes, that figure is higher than a Data Analyst or a Software Engineer. The reason, they are highly in demand. A Data Scientist needs to work on different stacks. He/ She needs to understand the questions that the business team needs answers for, from the data. He/ She needs to know coding and statistics to handle the data. And should have the infrastructure know how to make decisions based on which cloud infra to run an algorithm on. They also need to have visualization skills to know which is the best way to present the data and findings to the wider audience.


Fig: Number of “Data Scientist job postings” per 1 million job posts on Indeed.

Since it is a highly specialized job title that requires a diverse skill set. It is understandable that the market has always faced a crunch of candidates. While the market has automated tools options, we still need data-scientists to use them. Today, the Fortune 500 companies have scooped up a large percentage of Data Scientists. The remaining have gone to well-funded startups. For the rest of the companies, there are a few fish in the pond and too many fishermen.

The Impact of COVID19

The current generations (Gen X, Y, and Z) have never experienced a pandemic before. And understandably we have no usable data from a previous one. This leads to an increase in the demand for Data Scientists. There is a requirement to simulate the circumstances and deduce consumer demand. Uncertain behavior, like high sale of paper tissues, can also lead to a decrease in sales later on, when everyone has a stockpile. Thus, market data needs analyzing in real-time today to best predict what tomorrow will bring. Even the spread of the virus itself being analyzed by Data Scientists to understand how fast-spreading and what are the means spreading through.

How Can You Become A Data Scientist?

A large percentage of data scientists who are working on data today were Software Engineers or Data Analysts, who learned the extra skills necessary through online courses or MOOCs on websites like Coursera and Udacity. There’s also a dedicated website, Kaggle, which has developed into the world’s large platform for Data Scientists and Machine Learning Engineers. Companies like Google and Lyft host competitions on the platform, where the team with the most optimized solution for a machine learning problem wins. There are also discussion boards, job-board, and more. All the resources that you may need are already there; you just need to take the leap.

JobsPikr is your-go-to-source for the job market insights. It is a customizable job feed and analytics solution that has the widest range of historical and active jobs from the job market across the globe. If you liked the content above leave your valuable feedback in the comments section below.

Scraping Indeed Job Data, Using Python

Indeed is one of the most popular job websites in the market today. It is a job aggregating website available in 60+ countries and covers multiple job boards, staffing firms, and company career pages. Scraping job sites like Indeed can help you access the latest job data, analyze job trends, and automate job boards. Indeed allows you to search job-based on location and keywords. These keywords can be a job title, skills, or any search term in the job listing. We will be using these two search boxes along with the number of pages of search results to crawl Indeed and extract the data.

Where is the Code for Indeed Job Scraping?

First, you need to have the requirements installed to begin the job scraping from Indeed. These are Python3.7 or higher, BeautifulSoup, and a code editor. Once that is done you can save the code below to a file with the “.py “ extension and run it. But before we go into running the code, let us first understand the code itself.

It is the “main” method, where the execution starts. We take three inputs from the user

 name of the city for which he or she wants job listings, keyword, and the number of pages of search results that are desired. Once we have these data points, we create the URL that needs to be hit for getting the search results. The “scrape_data” function is called next, which loops over the number of pages of search results that we want and calls the “get_data_from_webpage” function to extract job data from Indeed’s webpages.

In the “get_data_from_webpage” function, we extract the data for all job posts on a single webpage by looping over all the job posts on a single webpage of search results. We also strip the job post content to just the first 100 characters. You can change that piece of code so that you can get the required data at hand. In turn, the “extract_data_points” function called for every job post on a single page. It captures various data points by going into the specific job post links on Indeed. It captured the HTML data and converts it into a BeautifulSoup object, which is then parsed.

In simple terms, there are three levels of web data scraping on Indeed for job posts:

  1. We loop through the n pages of search results
  2. Then we loop through all the job posts in a single web page
  3. We scrape the data for a single webpage by going to its link
Jobs Scraping Software

Once the code runs on the number of pages we selected, we get an array of dicts where each dict contains the data of a single job post. We tested this code using these following values that you can see below-

Indeed Scraper

The Output Of Job Scraping On Indeed

For the input data that we showed above. The below JSON is what was received as a result. You can see that there are just three job posts. But that is because we truncated the list to fit the blog. In reality, we scraped around seven job posts for the given search terms on page 1 of the search results. The data points that we captured for each job post are:

All the data points are self-explanatory. We specifically captured these because we believe these are most important for job applicants and job analysts.

Scraping Indeed

Certain data points like salaries may seem to be missing. The reason is that a large number of companies did not have the salary in the job posts and those who have it, it is in their job details itself.

Can This Work at An Enterprise Level?

This is a DIY code and cannot run at an enterprise level, that needs Indeed crawling and the job data scraped 24×7. The site will block you, the code is likely to break at some job listing with a different format, and more issues that can plague your production system.

For enterprise requirements, we have a professional job scraping solution in JobsPikr. We can automate job scraping and delivery to help you in your efforts at building a job board or in conducting research using job data.

JobsPikr Product Update: New And Exciting Features Added

The job market has never seen a dull day. With the world adapting to the new normal, we’ve seen the jobs’ demand and supply gap widen even further. Today, enterprises are looking at elegant solutions to help them bridge this gap. Gone are the days, when users can afford to lose time on vetting the scrapped job boards. The need of the hour is quality and exclusive job data, and the need is now! Let us take a look at the new JobsPikr Product Update that we are about to roll out this month.

So now it is, as JobsPikr launches a series of exciting new features to help you gain access to and make a better sense of the job data. Here’s how.

Fortune 500 Dataset

Our amazing set of users have always asked if they could receive data directly from career pages of companies, apart from popular job boards of course. We have heard from you. From now on, you’d be able to subscribe to job data feed directly from Fortune 500 companies’ career pages! This is a huge leap, given the technical complexities involved in setting up this data pipeline and its ongoing maintenance. It sets up JobsPikr as the unique and go-to-solution for exclusive job datasets of incredible value.

With this JobsPikr Product Update, this dataset is available at the top of the line Enterprise Plan. Existing customers may contact their respective Sales / Account Managers or Customer Support to gain access. We do have some early bird discounts going on as well.

Remote Work Flag

With a lot of jobs going remote, we recently launched a new field in our data, viz., is_remote. Our automated tagging system reads through the job posting content and determines if a given job is remote in nature or not. This is a Boolean field that would have True or False values.

So no more wastage of your precious time, in determining the location for ‘Remote’ and ‘Gig jobs’.

Salary Inference

Until now, we provide salary information as a text field – is available from the source websites We realized the limitation with this approach and upgraded it. Now onwards, we will also be delivering salary inferences split across four separate, query-able fields: inferred_salary_currency, inferred_salary_from, inferred_salary_to, inferred_salary_time_unit.

This unlocks a world of new possibilities in terms of analyzing salary trends and even querying for jobs based on a particular salary range.

As of now, we are only handling cases where salary information provided as a separate field from the source websites we collect data from. The model will gradually extend to infer and predict salaries based on job description text and various other related criteria.

Multi-Value Fields

Some of the fields we provide like location (i.e. city, state, and country) and email address tend to have multiple values for a given record. We realized it interferes not just with our location inferences (i.e. inferred_city, inferred_state, and inferred_country), but also with downloading and importing data.

Hereon, these fields provided as multi-value fields. In other words, these fields will be an array if you’re downloading data in JSON format, and as nested fields if consuming data in XML format. For CSV, it will continue to be pipe-separated values.

Note: Existing users will have to update their import process to handle these fields.

We’re thrilled to share these features with you and hope it helps you make the best use of JobsPikr. We have many more features in the works – which will soon be rolled out. In case you have any questions or concerns, mail us at

Job Growth in The United States During COVID-19

The Net Job Growth in The United States:

The United States, the most powerful democracy in the world that focuses deeply on job growth and the unemployment numbers of the country. So much so that presidents judged based on the number of jobs created during their tenure. Hence, in the times of COVID, it makes sense to discuss the growth in the number of jobs in the USA. Its fall during times of crisis, and what will be the effects of COVID 19 on the entire scenario at hand is something to ponder upon.

Another important denominator today is the US-China trade spat that has created a lot of tension in the domestic market. One thing to note is that since the Bureau of Labor Statistics calculates all the job data as anon-farm job data only, that is what we will be referring to wherever we speak of jobs unless mentioned otherwise.

The Net Job Growth:

Job Creation is very important for any nation to thrive. Only when more job creation is constant. More people can contribute to the economy and nation-building. Job data gathering in the United States started way back in 1915 when a handful of manufacturers asked by the government to share the employment and salary data of their employees. In 1919 was the time when we first started seeing monthly data on employment and average earnings of the people of the United States of America.

One of the reasons behind the rapid growth of the USA attributed to the factor that net jobs grew by more than 5 times in almost a decade. The numbers stood at 27.1 million in 1919 whereas in 2016 it was 143.1 million. But if you see the graphs and numbers, this growth has not been very steady. 1932 saw a massive drop in jobs of 11.3% and 1941 saw a record high of 12.9% job growth.

job growth
Fig: Job growth under different presidents. By Forecaster – Own work, CC BY-SA 4.0
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job growthFig: Job growth under different presidents. By Forecaster – Own work, CC BY-SA 4.0

The average growth in the US has been around 2.1%. Most of the private industries have captured a greater part of the pie growing from 50% to 71% by 2015. However, the manufacturing industry has seen its share trimmed from 37% to 14%. With time, the manufacturing industry has seen itself shrinking.

World War II specifically saw massive growth in employment, but it was an unnatural spike which later resulted in the subtraction of jobs. Of the entire gains in the US job-industry, the service sector has taken up more than 90%.

The US-China Trade War:

The bitter US-China trade war has had far-reaching effects across the globe. The dispute has seen more than billions of dollars in tariff imposed on each other’s goods. Negotiations have proven to be difficult and there have been a series of tariffs- July 18, Aug 18, Sep 18, May 19, and June 19, as of now. This has threatened both jobs and the economy of the USA. As per one study conducted by the Port of Los Angeles, as many as 1.5 million jobs threatened by the ongoing spat. It could also end up impacting almost $186 billion of economic activity.

As prices rise, sometimes as much as 25%, consumption could decrease and the demand for the raw goods could fall. This would, in turn, lead to a decrease in the workforce in multiple industries. The damage that this war could cause to the domestic job market, coupled with the Coronavirus Pandemic is monstrous in number.

2008 Financial Crisis vs COVID-19:

As per most reports, the US unemployment rate has jumped to an all-time high of 20%. As many as 39 million Americans rendered jobless by the pandemic. And things have gotten worse in just 3 months of COVID when compared to 2 years of The Great Recession. At the peak of lockdown enforcements, around 6 million people applied for unemployment benefits in a single week.

job growth
Fig: Number of unemployment claims over the year in the USA (monthly stats).

The graph above shows how unemployment numbers reached a record high in 2020. The number of unemployment claims in a single month was more than 10 times that of the 2008 Financial Crisis. But this graph does not give us the whole picture at a single glance. To calculate the total jobs lost in the 2008 crisis, you need to calculate the area under the graph during the crisis.

Impact Of COVID on The Job Growth:

As for the COVID situation, it is more sudden, and hence the spike. More jobs lost at a single moment this time, and a better comparison made once things stabilize. While the picture is grimmer than 2008, it is not as bad as the graph makes it out to be.

job growth
Fig: Number of unemployment claims over the year in the USA (monthly stats).

Before COVID hit us, until April, you can see that the weekly average for unemployment claims hit around 350,000. By April, it had hit 6.8 million, but the numbers have been steadily decreasing since then. It took just 3 months for it to drop down to 1.43million and as time passes. The numbers will get back to the previous average. But there lies a great amount of uncertainty.

While some vaccines are already in the final stage of their clinical trials. It is to be noted that from this stage to actual vaccines being given to the public- it can take more than a year. Along with that, cases of re-infection from various parts of the globe has increased fear among the public. Chances of a second wave of infections are still being discussed by experts.

The Future:

Recovery has been quick, and both the market and jobs have bounced back with some reporting that. June saw a nonfarm employee growth of 4.8million compared to the expected 2.9million. The unemployment rate itself has fallen to 11% from its worst of around 15%. While these figures do look good, the uncertainty of the pandemic and the unknown timelines of the vaccine do bring in some level of doubt.

But if you look at the market, it seems optimistic now, and if the momentum continues, we can expect better figures by the first half of the next year. At the same time, presidential elections are around the block, and we can expect its outcome to have a significant effect on government policies, which in turn shall impact the net job growth of the US.

JobsPikr is your-go-to-source for the job market insights. It is a customizable job feed and analytics solution that has the widest range of historical and active jobs from the job market across the globe. If you liked the content above leave your valuable feedback in the comments section below.