diff --git a/.gitignore b/.gitignore
new file mode 100644
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--- /dev/null
+++ b/.gitignore
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+# Python
+__pycache__/
+*.pyc
+*.pyo
+*.csv
+
diff --git a/scripts/scrape_fake_jobs.py b/scripts/scrape_fake_jobs.py
new file mode 100755
index 0000000..7638ccb
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+++ b/scripts/scrape_fake_jobs.py
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+#!/usr/bin/env python3
+"""
+Scrape fake job postings and save them to fake_jobs.csv
+
+This script demonstrates using BeautifulSoup's class-based find/find_all
+features (see https://blog.apify.com/beautifulsoup-find-by-class/) to extract
+job postings from the Real Python "fake jobs" demo page and write them to a CSV.
+
+Output CSV: fake_jobs.csv
+Columns (header): Job Title, Company, Location, Date Posted
+
+Usage:
+    python3 scripts/scrape_fake_jobs.py
+
+Requirements:
+    pip install requests beautifulsoup4
+
+The script uses class-based selectors to extract:
+    - Job Title: <h2 class="title">
+    - Company: <h3 class="company">
+    - Location: <p class="location">
+    - Date Posted: <time> element (text content)
+
+Each job is wrapped in a <div class="card-content"> container.
+The script includes fallbacks for slight HTML variations to ensure robustness.
+"""
+
+from __future__ import annotations
+import csv
+import sys
+from typing import List, Dict
+
+import requests
+from bs4 import BeautifulSoup
+
+SOURCE_URL = "https://realpython.github.io/fake-jobs/"
+OUTPUT_CSV = "fake_jobs.csv"
+CSV_HEADERS = ["Job Title", "Company", "Location", "Date Posted"]
+
+
+def fetch_page(url: str) -> str:
+    """
+    Fetch HTML content from the given URL.
+    
+    Args:
+        url: The URL to fetch
+        
+    Returns:
+        The HTML content as a string
+        
+    Raises:
+        requests.HTTPError: If the request fails
+    """
+    resp = requests.get(url)
+    resp.raise_for_status()
+    return resp.text
+
+
+def _get_text(elem) -> str:
+    """
+    Safely extract text from a BeautifulSoup element.
+    
+    Args:
+        elem: A BeautifulSoup element or None
+        
+    Returns:
+        The stripped text content, or empty string if elem is None
+    """
+    return elem.get_text(strip=True) if elem else ""
+
+
+def parse_jobs(html: str) -> List[Dict[str, str]]:
+    """
+    Parse job postings from HTML content.
+    
+    Uses class-based find/find_all per https://blog.apify.com/beautifulsoup-find-by-class/
+    to extract job data with fallbacks for HTML variations.
+    
+    Args:
+        html: The HTML content to parse
+        
+    Returns:
+        A list of dictionaries, each containing job data with keys:
+        "Job Title", "Company", "Location", "Date Posted"
+    """
+    soup = BeautifulSoup(html, "html.parser")
+
+    # The Real Python fake-jobs demo wraps each job card inside 
+    # <div class="card-content">. We use class_ parameter for find_all
+    # as demonstrated in https://blog.apify.com/beautifulsoup-find-by-class/
+    job_cards = soup.find_all("div", class_="card-content")
+    
+    jobs = []
+    for card in job_cards:
+        # Extract job title from <h2 class="title">
+        # Fallback: try "title" class first, then any h2
+        title_elem = card.find("h2", class_="title") or card.find("h2")
+        title = _get_text(title_elem)
+        
+        # Extract company from <h3 class="company">
+        # Fallback: try "company" class first, then any h3
+        company_elem = card.find("h3", class_="company") or card.find("h3")
+        company = _get_text(company_elem)
+        
+        # Extract location from <p class="location">
+        # Fallback: try "location" class first, then first p tag
+        location_elem = card.find("p", class_="location") or card.find("p")
+        location = _get_text(location_elem)
+        
+        # Extract date posted from <time> element
+        # Fallback: try time tag first, then look for datetime attribute or text
+        time_elem = card.find("time")
+        if time_elem:
+            # Prefer datetime attribute if available, else use text content
+            date_posted = time_elem.get("datetime", "").strip() or _get_text(time_elem)
+        else:
+            date_posted = ""
+        
+        # Only add job if we have at least a title
+        if title:
+            jobs.append({
+                "Job Title": title,
+                "Company": company,
+                "Location": location,
+                "Date Posted": date_posted
+            })
+    
+    return jobs
+
+
+def write_csv(jobs: List[Dict[str, str]], filename: str) -> None:
+    """
+    Write job data to a CSV file.
+    
+    Args:
+        jobs: List of job dictionaries
+        filename: Output CSV filename
+    """
+    with open(filename, "w", newline="", encoding="utf-8") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=CSV_HEADERS)
+        writer.writeheader()
+        writer.writerows(jobs)
+
+
+def main() -> int:
+    """
+    Main function to scrape jobs and save to CSV.
+    
+    Returns:
+        0 on success, 1 on error
+    """
+    try:
+        print(f"Fetching job postings from {SOURCE_URL}...")
+        html = fetch_page(SOURCE_URL)
+        
+        print("Parsing job data...")
+        jobs = parse_jobs(html)
+        
+        if not jobs:
+            print("Warning: No jobs found on the page.", file=sys.stderr)
+            return 1
+        
+        print(f"Found {len(jobs)} job postings.")
+        
+        print(f"Writing to {OUTPUT_CSV}...")
+        write_csv(jobs, OUTPUT_CSV)
+        
+        print(f"Success! Job data saved to {OUTPUT_CSV}")
+        return 0
+        
+    except requests.RequestException as e:
+        print(f"Error fetching page: {e}", file=sys.stderr)
+        return 1
+    except Exception as e:
+        print(f"Unexpected error: {e}", file=sys.stderr)
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())