How to Scrape Monster Jobs with Python

Monster.com is one of the top sites for job seekers and employers, with millions of job listings across diverse industries. It’s a great place for job hunters and employers looking for employees. By scraping Monster.com, you can get tons of job data to use for analytics, to monitor the job market, or to build a custom job search tool.

Monster.com attracts more than 6.7 million visitors monthly and hosts thousands of active job listings, making it a goldmine of helpful information. Yet, its ever-changing nature means you need an intelligent strategy to scrape Monster.com. This guide will show you how to set up a Python environment, build a Monster.com page scraper, and make it better with the Crawlbase Crawling API. This API helps deal with tricky stuff like JavaScript rendering and endless scroll pagination.

This guide will provide you with the knowledge and tools you need to scrape Monster.com without any hurdles. Let’s get started!

Table Of Contents

1. Why Scrape Monster.com?
2. Key Data Points to Extract from Monster.com
3. Crawlbase Crawling API for Monster.com Scraping

• Why Use Crawlbase Crawling API?
• Crawlbase Python Library

4. Setting Up Your Python Environment

• Installing Python
• Setting Up a Virtual Environment
• Installing Required Libraries
• Choosing the Right IDE

5. Scraping Monster.com Job Listings

• Inspecting Monster.com Job Listing Page
• Writing the Monster.com Listing Scraper
• Handling Scroll Pagination
• Storing Data in a JSON File
• Complete Code Example

6. Scraping Monster.com Job Pages

• Inspecting Monster.com Job Page
• Writing the Monster.com Job Page Scraper
• Storing Data in a JSON File
• Complete Code Example

7. Final Thoughts
8. Frequently Asked Questions

Why Scrape Monster.com?

Scraping Monster.com gives you access to a huge amount of job market data, offering key insights that are hard to gather manually. By automating how you collect job listings, you can get info like job titles, locations, salaries, company names, and job descriptions. This data is key to understanding current market trends and making choices based on facts.

You might be a recruiter who wants to look at competitor job posts, a data analyst studying job trends, or a job seeker who wants to keep track of changes across industries. If so, Monster.com is a great source. With millions of job listings, the platform is perfect for anyone who needs fresh and detailed job data.

Making this data gathering automatic saves time and gives reliable correct info. Rather than looking through and getting data by hand, you can zero in on making sense of it. This lets you build useful things like job finders, trend trackers, or ways to compare pay.

Key Data Points to Extract from Monster.com

When you’re pulling data from Monster.com, you should zero in on getting the key info that gives you useful insights. Here’s what you need to grab:

1. Job Title: This tells you what jobs are open right now.
2. Job Description: This sums up the job, covering duties, skills needed, work experience required, and any perks.
3. Company Name: Identifies the employers offering the jobs.
4. About the Company: This gives a quick look at the company’s story and goals.
5. Company Website: The company’s main web page.
6. Company Size: This tells you how big the company is, like how many people work there.
7. Year Founded: The year the company was established.
8. Location: Shows where the job is based, helping filter positions by region.
9. Salary Information: If it’s there, this helps you know what pay to expect.
10. Job Posting Date: This enables you to see how new the job ads are.
11. Job Type: Whether the job is full-time, part-time, contract, etc.
12. Application Link: This takes you straight to where you can apply for the job.
13. Industry: This points out what field the job is in, like tech, healthcare, or money stuff.
14. Required Skills: Skills needed for the role.
15. Job ID: A unique identifier for each job posting, useful for tracking and updating listings.

Having a clear idea of what to extract before starting your Monster.com scraper helps you stay focused and ensures that your scraper collects meaningful data.

Crawlbase Crawling API for Monster.com Scraping

When scraping Monster.com, handling JavaScript-rendered content and navigating dynamic pages can be challenging with simple scraping techniques. That’s where the Crawlbase Crawling API comes in handy. This tool helps manage these complexities by rendering JavaScript and handling pagination efficiently.

Why Use Crawlbase Crawling API?

Monster.com uses JavaScript to load job postings and other important content dynamically. The old-fashioned scraping methods that just pull down static HTML don’t get this kind of stuff. The Crawlbase Crawling API avoids these restrictions by mimicking as actual browser, allowing all JavaScript-rendered components to be loaded and available.

Monster.com relies on JavaScript to load job listings and other content dynamically. Traditional scraping methods, which only fetch static HTML, often fail to capture this content. Crawlbase Crawling API overcomes these limitations by simulating a real browser, ensuring that all JavaScript-rendered elements are loaded and accessible.

Key Features of Crawlbase Crawling API

1. JavaScript Rendering: Crawlbase can handle the execution of JavaScript on the page, allowing you to scrape data that is loaded dynamically.
2. Avoid IP Blocking and CAPTCHAs: Crawlbase automatically rotates IPs and bypasses CAPTCHAs, allowing uninterrupted scraping of Monster.com without facing IP blocks or CAPTCHA challenges.
3. Handling Pagination: The API allows for all kinds of paging methods, including the “infinite scrolling” found on job boards like Monster.
4. Request Options: Customize your scraping requests with options for handling cookies, setting user agents, and more, making your scraping efforts more robust and reliable.

Crawlbase Python Library

Crawlbase has a Python library that makes web scraping a lot easier. This library requires an access token to authenticate. This token which you can get by registering an account with crawlbase.

Here’s an example function demonstrating how to use the Crawlbase Crawling API to send requests:

from crawlbase import CrawlingAPI

# Initialize Crawlbase API with your access token
crawling_api = CrawlingAPI({ 'token': 'YOUR_CRAWLBASE_TOKEN' })

def make_crawlbase_request(url):
    response = crawling_api.get(url)

    if response['headers']['pc_status'] == '200':
        html_content = response['body'].decode('utf-8')
        return html_content
    else:
        print(f"Failed to fetch the page. Crawlbase status code: {response['headers']['pc_status']}")
        return None

Note: Crawlbase offers two types of tokens:
_ Normal Token for static sites.
_ JavaScript (JS) Token for dynamic or browser-based requests.

For scraping dynamic sites like Monster.com, you’ll need the JS Token. Crawlbase provides 1,000 free requests to get you started, and no credit card is required for this trial. For more details, check out the Crawlbase Crawling API documentation.

In the next section, we’ll guide you through setting up a Python environment for this project. Let’s get started with the setup!

Setting Up Your Python Environment

We need to set up your Python environment before creating the Monster.com scraper. This section covers the essentials: installing Python and libraries, setting up a virtual environment, and choosing an IDE.

Installing Python

First, ensure you have Python installed on your computer. Python is a very flexible language used for many things, one of which is web scraping. You can download it from the official Python website. Follow the installation instructions specific to your operating system.

Setting Up a Virtual Environment

A virtual environment makes it easier to manage project dependencies without affecting other Python projects. Here’s how to set one up:

1. Create a Virtual Environment: Navigate to your project directory in the terminal and run:

python -m venv monster_env

2. Activate the Virtual Environment:

• On Windows:

  monster_env\Scripts\activate

• On macOS/Linux:

  source monster_env/bin/activate

Installing Required Libraries

With that the virtual environment is activated, you’ll have to install a couple of libraries to aid in web scraping and data processing.

1. Crawlbase: The main library for sending requests with the Crawlbase Crawling API.
2. BeautifulSoup4: For parsing HTML and XML documents.
3. Pandas: For handling and analyzing data.

You can install these libraries using pip. Open your terminal or command prompt and run:

pip install crawlbase beautifulsoup4 pandas

Choosing the Right IDE

An Integrated Development Environment (IDE) makes coding easier by providing useful features like syntax highlighting, debugging tools, and project management. Here are a few popular IDEs for Python development:

• PyCharm: A professional IDE for Python with lots of really cool features.
• Visual Studio Code (VS Code): A nice lightweight general use editor with good Python support via plugins.
• Jupyter Notebook: Good for interactive coding and data analysis, especially when you need to visualize the data.

Choose an IDE that fits your preferences and workflow to streamline your coding experience.

Now that we have Python installed, libraries downloaded, and the development environment configured, we can proceed to the next phase of writing Monster.com pages scraper.

Scraping Monster.com Job Listings

Let’s start with web scraping Monster.com job listings with Python. Since Monster.com uses dynamic content loading and scroll-based pagination, simple scraping methods won’t be enough. We’ll be using Crawlbase’s Crawling API to take care of the JavaScript rendering and the scroll pagination, so that we scrape maximum job postings.

Inspecting Monster.com Job Listing Page

The first thing to do is to examine the HTML structure of the job posting page before jumping into the code. Knowing the hierarchy allows us to figure out the proper CSS selectors to get job information, such as the job title, company, location, and job URL.

1. Visit the URL: Open Monster.com and navigate to a job listing page.
2. Open Developer Tools: Right-click anywhere on the page and select “Inspect” to open the Developer Tools.
3. Identify Key Elements: Job listings are typically found within <article> elements with the attribute data-testid="svx_jobCard" inside a <div> with the ID JobCardGrid. The key elements include:
   • Job Title: Found within an <a> tag with the attribute data-testid="jobTitle".
   • Company Name: Found within a <span> tag with the attribute data-testid="company".
   • Location: Located within a <span> tag with the attribute data-testid="jobDetailLocation".
   • Job Link: Found within the href attribute of the job title <a> tag with data-testid="jobTitle".

Writing the Monster.com Listing Scraper

Now, let’s write the scraper to extract job details from Monster.com. We’ll use the Crawlbase Crawling API, which simplifies handling dynamic content.

Here’s the code:

from crawlbase import CrawlingAPI
from bs4 import BeautifulSoup
import json

# Initialize Crawlbase API with your access token
crawling_api = CrawlingAPI({'token': 'YOUR_CRAWLBASE_TOKEN'})

def scrape_monster_jobs(url):
    options = {
        'ajax_wait': 'true',
        'page_wait': '5000'  # Wait for the page to fully load
    }
    response = crawling_api.get(url, options)

    if response['headers']['pc_status'] == '200':
        soup = BeautifulSoup(response['body'], 'html.parser')
        job_cards = soup.select('div#JobCardGrid article[data-testid="svx_jobCard"]')

        all_jobs = []
        for job in job_cards:
            title = job.select_one('a[data-testid="jobTitle"]').text.strip() if job.select_one('a[data-testid="jobTitle"]') else ''
            company = job.select_one('span[data-testid="company"]').text.strip() if job.select_one('span[data-testid="company"]') else ''
            location = job.select_one('span[data-testid="jobDetailLocation"]').text.strip() if job.select_one('span[data-testid="jobDetailLocation"]') else ''
            link = job.select_one('a[data-testid="jobTitle"]')['href'] if job.select_one('a[data-testid="jobTitle"]') else ''

            job_listings.append({
                'Job Title': title,
                'Company': company,
                'Location': location,
                'Job Link': link
            })

        return job_listings
    else:
        print(f"Failed to fetch data. Status code: {response['headers']['pc_status']}")
        return None

The options parameter includes settings like ajax_wait for handling asynchronous content loading and page_wait to wait 5 seconds before scraping, allowing all elements to load properly. You can read about Crawlbase Crawling API parameters here.

Handling Scroll Pagination

Monster.com uses scroll-based pagination to load more job listings dynamically. To capture all job listings, we’ll utilize the scroll and scroll_interval parameters provided by Crawlbase Crawling API.

• scroll=true: Enables scroll-based pagination.
• scroll_interval=60: Sets the scroll duration to 60 seconds, which is the maximum allowed time. Since we added time for scrolling, there’s no need to explicitly set page_wait.

Here’s how you can handle it:

def scrape_monster_with_pagination(url):
    options = {
        'ajax_wait': 'true',
        'scroll': 'true',  # Enables scroll pagination
        'scroll_interval': '60'  # Scroll duration set to 60 seconds
    }

    response = crawling_api.get(url, options)
    if response['headers']['pc_status'] == '200':
        soup = BeautifulSoup(response['body'], 'html.parser')
        job_cards = soup.select('div#JobCardGrid article[data-testid="svx_jobCard"]')

        all_jobs = []
        for job in job_cards:
            title = job.select_one('a[data-testid="jobTitle"]').text.strip() if job.select_one('a[data-testid="jobTitle"]') else ''
            company = job.select_one('span[data-testid="company"]').text.strip() if job.select_one('span[data-testid="company"]') else ''
            location = job.select_one('span[data-testid="jobDetailLocation"]').text.strip() if job.select_one('span[data-testid="jobDetailLocation"]') else ''
            link = job.select_one('a[data-testid="jobTitle"]')['href'] if job.select_one('a[data-testid="jobTitle"]') else ''

            all_jobs.append({
                'Job Title': title,
                'Company': company,
                'Location': location,
                'Job Link': link
            })

        return all_jobs
    else:
        print(f"Failed to fetch data. Status code: {response['headers']['pc_status']}")
        return None

Storing Data in a JSON File

Once you have scraped the job data, you can easily store it in a JSON file for future use or analysis:

def save_to_json(data, filename='monster_jobs.json'):
    with open(filename, 'w') as file:
        json.dump(data, file, indent=4)
    print(f"Data saved to {filename}")

# Example usage after scraping
if jobs:
    save_to_json(jobs)

Complete Code Example

Here’s the full code combining everything discussed:

from crawlbase import CrawlingAPI
from bs4 import BeautifulSoup
import json

# Initialize Crawlbase API with your access token
crawling_api = CrawlingAPI({'token': 'YOUR_CRAWLBASE_TOKEN'})

def scrape_monster_with_pagination(url):
    options = {
        'ajax_wait': 'true',
        'scroll': 'true',  # Enables scroll pagination
        'scroll_interval': '60'  # Scroll duration set to 60 seconds
    }

    response = crawling_api.get(url, options)
    if response['headers']['pc_status'] == '200':
        soup = BeautifulSoup(response['body'], 'html.parser')
        job_cards = soup.select('div#JobCardGrid article[data-testid="svx_jobCard"]')

        all_jobs = []
        for job in job_cards:
            title = job.select_one('a[data-testid="jobTitle"]').text.strip() if job.select_one('a[data-testid="jobTitle"]') else ''
            company = job.select_one('span[data-testid="company"]').text.strip() if job.select_one('span[data-testid="company"]') else ''
            location = job.select_one('span[data-testid="jobDetailLocation"]').text.strip() if job.select_one('span[data-testid="jobDetailLocation"]') else ''
            link = job.select_one('a[data-testid="jobTitle"]')['href'] if job.select_one('a[data-testid="jobTitle"]') else ''

            all_jobs.append({
                'Job Title': title,
                'Company': company,
                'Location': location,
                'Job Link': link
            })

        return all_jobs
    else:
        print(f"Failed to fetch data. Status code: {response['headers']['pc_status']}")
        return None

def save_to_json(data, filename='monster_jobs.json'):
    with open(filename, 'w') as file:
        json.dump(data, file, indent=4)
    print(f"Data saved to {filename}")

if __name__ == "__main__":
    base_url = 'https://www.monster.com/jobs/search?q=Java+Developers&where=New+York&page=1&so=p.s.lh'
    jobs = scrape_monster_with_pagination(base_url)

    if jobs:
        save_to_json(jobs)

This code shows you how to set up the scraper, handle scroll pagination, and store the data in a structured JSON format, making it easy for you to use the scraped data later on.

Example Output:

[
    {
        "Job Title": "Java Developer(Core Java)",
        "Company": "Georgia IT Inc.",
        "Location": "New York, NY",
        "Job Link": "https://www.monster.com/job-openings/java-developer-core-java-new-york-ny--1abe38e2-8183-43d3-a152-ecdf208db3bf?sid=3a00f5d1-d543-4de0-ab00-0f9e9c8079f8&jvo=m.mo.s-svr.1&so=p.s.lh&hidesmr=1"
    },
    {
        "Job Title": "Java Backend Developer(Java, Spring, Microservices, Maven)",
        "Company": "Diverse Lynx",
        "Location": "Manhattan, NY",
        "Job Link": "https://www.monster.com/job-openings/java-backend-developer-java-spring-microservices-maven-manhattan-ny--7228b274-60bb-41d7-b8d9-8a51ff8c8d1c?sid=3a00f5d1-d543-4de0-ab00-0f9e9c8079f8&jvo=m.mo.s-svr.2&so=p.s.lh&hidesmr=1"
    },
    {
        "Job Title": "Java Backend Developer(Java, Spring, Microservices, Maven)",
        "Company": "Diverse Linx",
        "Location": "Manhattan, NY",
        "Job Link": "https://www.monster.com/job-openings/java-backend-developer-java-spring-microservices-maven-manhattan-ny--2d652118-1c17-43e3-8cef-7b940a8b0490?sid=3a00f5d1-d543-4de0-ab00-0f9e9c8079f8&jvo=m.mo.s-svr.3&so=p.s.lh&hidesmr=1"
    },
    {
        "Job Title": "JAVA FULL STACK DEVELOPER",
        "Company": "HexaQuEST Global",
        "Location": "Brooklyn, NY",
        "Job Link": "https://www.monster.com/job-openings/java-full-stack-developer-brooklyn-ny--c60fb5f3-5adf-43a7-bfac-03c93853dd4e?sid=3a00f5d1-d543-4de0-ab00-0f9e9c8079f8&jvo=m.mo.s-svr.4&so=p.s.lh&hidesmr=1"
    },
    .... more
]

Scraping Monster.com Job Pages

After collecting job listings from Monster.com, the next step is to scrape detailed information from each job page. In this section, we’ll guide you on how to extract specific details like job descriptions, requirements, and company information from the job pages.

Inspecting Monster.com Job Page

To begin scraping a job page, you need to inspect its HTML structure to identify the elements that hold the data you need. Here’s how you can do it:

1. Visit a Job Page: Click on a job listing to open its detailed page.
2. Open Developer Tools: Right-click on the page and select “Inspect” to open the Developer Tools.
3. Identify Key Elements: Look for elements containing information such as:
   • Job Title: Found within an <h2> tag with the attribute data-testid="jobTitle".
   • Job Description: Usually located within a <div> tag with the attribute data-testid="svx-description-container-inner".
   • Numbers & Facts: Typically found in a table row (<tr>) inside a table with the attribute data-testid="svx-jobview-details-table".
   • About Company: Located within a <div> tag with a class containing "about-styles__AboutCompanyContainerInner".

Writing the Monster.com Job Page Scraper

Once you know the structure, you can create a scraper to extract these details. The Crawlbase Crawling API will help handle any dynamic content while ensuring a smooth scraping process.

Here’s a professional example of the code:

from crawlbase import CrawlingAPI
from bs4 import BeautifulSoup
import json

# Initialize Crawlbase API with your access token
crawling_api = CrawlingAPI({'token': 'YOUR_CRAWLBASE_TOKEN'})

def scrape_job_page(url):
    options = {
        'ajax_wait': 'true',
        'page_wait': '5000'  # Wait for the page to fully load
    }

    response = crawling_api.get(url, options)
    if response['headers']['pc_status'] == '200':
        soup = BeautifulSoup(response['body'].decode('utf-8'), 'html.parser')

        job_title = soup.select_one('h2[data-testid="jobTitle"]').text.strip() if soup.select_one('h2[data-testid="jobTitle"]') else ''
        job_description = soup.select_one('div[data-testid="svx-description-container-inner"]').text.strip() if soup.select_one('div[data-testid="svx-description-container-inner"]') else ''
        numbersAndfacts = [{tr.select_one('td:first-child').text.strip() : tr.select_one('td:last-child').text.strip()} for tr in soup.select('table[data-testid="svx-jobview-details-table"] tr')] if soup.select('table[data-testid="svx-jobview-details-table"] tr') else []
        about_company = soup.select_one('div[class*="about-styles__AboutCompanyContainerInner"]').text.strip() if soup.select_one('div[class*="about-styles__AboutCompanyContainerInner"]') else ''

        job_details = {
            'Job Title': job_title,
            'Job Description': job_description,
            'Numbers & Facts': numbersAndfacts,
            'About Company': about_company
        }

        return job_details
    else:
        print(f"Failed to fetch data. Status code: {response['headers']['pc_status']}")
        return None

Storing Data in a JSON File

Once the job details are extracted, you can save the data in a JSON file for easier access and analysis:

def save_job_details_to_json(data, filename='job_details.json'):
    with open(filename, 'w') as file:
        json.dump(data, file, indent=4)
    print(f"Data saved to {filename}")

Complete Code Example

Here’s the full code that ties everything together:

from crawlbase import CrawlingAPI
from bs4 import BeautifulSoup
import json

# Initialize Crawlbase API with your access token
crawling_api = CrawlingAPI({'token': 'YOUR_CRAWLBASE_TOKEN'})

def scrape_job_page(url):
    options = {
        'ajax_wait': 'true',
        'page_wait': '5000'  # Wait for the page to fully load
    }

    response = crawling_api.get(url, options)
    if response['headers']['pc_status'] == '200':
        soup = BeautifulSoup(response['body'].decode('utf-8'), 'html.parser')

        job_title = soup.select_one('h2[data-testid="jobTitle"]').text.strip() if soup.select_one('h2[data-testid="jobTitle"]') else ''
        job_description = soup.select_one('div[data-testid="svx-description-container-inner"]').text.strip() if soup.select_one('div[data-testid="svx-description-container-inner"]') else ''
        numbersAndfacts = [{tr.select_one('td:first-child').text.strip() : tr.select_one('td:last-child').text.strip()} for tr in soup.select('table[data-testid="svx-jobview-details-table"] tr')] if soup.select('table[data-testid="svx-jobview-details-table"] tr') else []
        about_company = soup.select_one('div[class*="about-styles__AboutCompanyContainerInner"]').text.strip() if soup.select_one('div[class*="about-styles__AboutCompanyContainerInner"]') else ''

        job_details = {
            'Job Title': job_title,
            'Job Description': job_description,
            'Numbers & Facts': numbersAndfacts,
            'About Company': about_company
        }

        return job_details
    else:
        print(f"Failed to fetch data. Status code: {response['headers']['pc_status']}")
        return None

def save_job_details_to_json(data, filename='job_details.json'):
    with open(filename, 'w') as file:
        json.dump(data, file, indent=4)
    print(f"Data saved to {filename}")

if __name__ == "__main__":
    job_url = 'https://www.monster.com/job-openings/d94729d8-929e-4c61-8f26-fd480c31e931'
    job_details = scrape_job_page(job_url)

    if job_details:
        save_job_details_to_json(job_details)

The code above demonstrates how to inspect Monster.com job pages, extract specific job information, and store it in a JSON file.

Example Output:

{
  "Job Title": "Delivery Station Warehouse Associate",
  "Job Description": "Amazon Delivery Station Warehouse AssociateJob OverviewYou\u2019ll be part of the dedicated Amazon team at the delivery station \u2013 the last stop before we deliver smiles to customers. Our fast-paced, active roles receive trucks full of orders, then prepare them for delivery. You\u2019ll load conveyor belts, and transport and stage deliveries to be picked up by drivers.Duties & ResponsibilitiesSome of your duties may include:Receive and prepare inventory for deliveryUse technology like smartphones and handheld devices to sort, scan, and prepare ordersBuild, wrap, sort, and transport pallets and packagesYou\u2019ll also need to be able to:Lift up to 49 poundsReceive truck deliveriesView prompts on screens and follow direction for some tasksStand, walk, push, pull, squat, bend, and reach during shiftsUse carts, dollies, hand trucks, and other gear to move items aroundGo up and down stairs (where applicable)Work at a height of up to 40 feet on a mezzanine (where applicable)What it\u2019s like at an Amazon Delivery StationSurroundings. You\u2019ll be working around moving machines \u2013 order pickers, stand-up forklifts, turret trucks, and mobile carts.Activity. Some activities may require standing in one place for long periods, walking around, or climbing stairs.Temperature. Even with climate controls, temperatures can vary between 60\u00b0F and 90\u00b0F in some parts of the warehouse; on hot days, temperatures can be over 90\u00b0F in the truck yard or inside trailers.Noise level. It can get noisy at times. We provide hearing protection if you need it.Dress code. Relaxed, with a few rules to follow for safety. Comfortable, closed-toe shoes are required and protective safety footwear are required in select business units. Depending on the role or location, Amazon provides a $110 Zappos gift code towards the purchase of shoes, in order to have you prepared for your first day on the job.Why You\u2019ll Love AmazonWe have jobs that fit any lifestyle, state-of-the-art workplaces, teams that support and listen to our associates, and company-driven initiatives and benefits to help support your goals.Our jobs are nearby, with great pay, and offer work-life balance.Schedule flexibility. Depending on where you work, schedules may include full-time (40 hours), reduced-time (30-36 hours) or part-time (20 hours or less), all with the option of working additional hours if needed. Learn more about our schedules .Shift options. Work when it works for you. Shifts may include overnight, early morning, day, evening, and weekend. You can even have four-day workweeks and three-day weekends. Find out more about our shifts .Anytime Pay. You can instantly cash out up to 70% of your earnings immediately after your shift (for select employee groups). Learn more about Anytime Pay .Our workplace is unlike any other.State-of-the-art facilities. We have modern warehouses that are clean and well-organized.Safety. Your safety is important to us. All teams share safety tips daily and we make sure protective gear is available onsite. Please note, wearing a hard hat and/or safety shoes while working is a requirement for some roles at certain sites.Our team supports and listens to you.Culture. Be part of an inclusive workplace that offers a variety of DEI programs and affinity groups.Team environment. Work on small or large teams that support each other in a workplace that\u2019s been ranked among the best workplaces in the world.New skills. Depending on the role and location, you\u2019ll learn how to use the latest Amazon technology \u2013 including handheld devices and robotics.Our company supports your goals.Benefits. Many of our shifts come with a range of benefits that may include pay and savings options, healthcare, peace of mind for you and your family, and more.Career advancement. We have made a pledge to upskill our employees and offer a variety of free training and development programs, and we also have tuition support options for select employee groups. See where your Amazon journey can take you.Learn more about all the reasons to choose Amazon . A full list of benefits and criteria for each to be offered a successful applicant can be found here .Requirements:Candidates must be 18 years or older with the ability to understand and adhere to all job requirement and safety guidelines in English.How To Get StartedYou can begin by applying above. If you need help with your application or to learn more about our hiring process, you can find support here: https://hiring.amazon.com/hiring-process# /.Please note that if you already have an active application but are looking to switch to a different site, instead of applying for a new role, you can reach out to Application Help at https://hiring.amazon.com/contact-us#/ for next steps.If you have a disability and need an accommodation during the application and hiring process, including support for the New Hire Event, or need to initiate a request prior to starting your Day 1, please visit https://hiring.amazon.com/people-with-disabilities#/ or contact the Applicant-Candidate Accommodation Team (ACAT). You can reach us by phone at 888-435-9287, Monday through Friday, between 6 a.m. and 4 p.m. PT.Equal EmploymentAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.",
  "Numbers & Facts": {
    "Location": "Revere, MA",
    "Job Type": "Temporary, Part-time",
    "Industry": "Transport and Storage - Materials",
    "Salary": "$18.50 Per Hour",
    "Company Size": "10,000 employees or more",
    "Year Founded": "1994",
    "Website": "http://Amazon.com/militaryroles"
  },
  "About Company": "At Amazon, we don\u2019t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let\u2019s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life. \nWherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we\u2019ll add jobs that haven\u2019t been invented yet. \nIt\u2019s Always Day 1 \nAt Amazon, it\u2019s always \u201cDay 1.\u201d Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon\u2019s very first day \u2013 to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. \u201cStaying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,\u201d he wrote. \u201cA customer-obsessed culture best creates the conditions where all of that can happen.\u201d You can read the full letter here \nOur Leadership Principles\nOur Leadership Principles help us keep a Day 1 mentality. They aren\u2019t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they\u2019re discussing ideas for new projects, deciding on the best solution for a customer\u2019s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles"
}

Scrape Monster Jobs with Crawlbase

Extracting job postings from Monster.com can revolutionize your approach to gathering employment data. This guide walked you through setting up a Python environment crafting a Monster.com page scraper, handling scroll-based pagination, and storing your information.

For sites like Monster.com that rely on JavaScript, tools such as the Crawlbase Crawling API prove invaluable. You can tailor this method to suit your requirements, whether you’re building a personal job-tracking system or amassing data for a large-scale project. Keep expanding your knowledge and remember that effective web scraping hinges on selecting suitable tools and techniques.

To broaden your web scraping skills, think about checking out our next guides on extracting data from other key websites.

📜 How to Scrape Bloomberg
📜 How to Scrape Wikipedia
📜 How to Scrape Google Finance
📜 How to Scrape Google News
📜 How to Scrape Clutch.co

If you have any questions or feedback, our support team is always available to assist you on your web scraping journey. Happy Scraping!

Frequently Asked Questions

Q. Can I scrape job listings from Monster.com using basic Python libraries?

You might try to scrape Monster.com using basic Python libraries like requests and BeautifulSoup. However, the site relies on JavaScript to display content. This makes it hard for simple scraping methods to grab job listings well. To handle JavaScript and changing content, we suggest using Crawlbase Crawling API.

Q. How do I handle pagination while scraping Monster.com?

Monster.com loads more jobs as you scroll down the page. This is called scroll-based pagination. To handle this, you can use the  scroll and scroll_interval parameters in the Crawlbase Crawling API. This method makes sure your scraper acts like a real user scrolling and gets as many job listings as possible.

Q. Is scraping Monster.com legal?

You need to check the site’s terms of service to be certain about what you can scrape legally. It’s also important to scrape responsibly by respecting robots.txt rules and avoiding excessive requests that could strain their servers.