diff --git a/warn/scrapers/wv.py b/warn/scrapers/wv.py
new file mode 100644
index 00000000..b3362f3d
--- /dev/null
+++ b/warn/scrapers/wv.py
@@ -0,0 +1,92 @@
+from pathlib import Path
+
+import pdfplumber
+
+from .. import utils
+from ..cache import Cache
+
+__authors__ = ["Ash1R"]
+__tags__ = ["html", "pdf"]
+__source__ = {
+    "name": "Workforce West Virginia",
+    "url": "https://workforcewv.org/public-information/warn-notices/current-warn-notices",
+}
+
+
+def scrape(
+    data_dir: Path = utils.WARN_DATA_DIR,
+    cache_dir: Path = utils.WARN_CACHE_DIR,
+) -> Path:
+    """
+    Scrape data from west virginia workforce site.
+
+    It was a big pdf with all historical data,
+    And I used pdfplumber to extract the tables
+    And put them into wv.csv
+    """
+    cache = Cache(cache_dir)
+    headers = [
+        "Company",
+        "Address",
+        "Contact Information",
+        "Region",
+        "County",
+        "Date",
+        "Projected Date",
+        "Type",
+        "Number Affected",
+    ]
+    final_data = [headers]
+
+    wv_pdf = cache.download(
+        "WV_WARN_Notices_3-1-11_to_3-22-22.",
+        "https://workforcewv.org/images/files/PublicInfo/WV_WARN_Notices_3-1-11_to_3-22-22.pdf",
+    )
+    with pdfplumber.open(wv_pdf) as pdf:
+        for i in pdf.pages:
+            tables = i.find_tables()
+            for j in tables:
+                data = j.extract(x_tolerance=3, y_tolerance=3)
+                company = ""
+                companydone = False
+                row = []
+                for k in range(len(data)):
+                    if data[k][0] is not None:
+                        header_whitelist = [
+                            "Contact Information",
+                            "Region",
+                            "County",
+                            "Date of Notice",
+                            "Projected Date",
+                            "Closure/Mass Layoff",
+                            "Number Affected",
+                        ]
+
+                        if data[k][0].strip() in header_whitelist:
+                            row.append(data[k][1].strip())
+
+                        elif data[k][0].strip() == "Address":
+                            if not companydone:
+                                row.append(company)
+                                companydone = True
+                            row.append(data[k][1].strip())
+
+                        elif data[k][0].strip() == "Company":
+                            company = company + data[k][1].strip()
+
+                    elif ((data[k][0] is None) and (k != 0)) or (
+                        data[k][0] == "None" and k != 0
+                    ):
+                        for company_names in data[k]:
+                            if company_names is not None:
+                                company = company + ", " + company_names.strip()
+
+                final_data.append(row)
+
+    output_csv = data_dir / "wv.csv"
+    utils.write_rows_to_csv(output_csv, final_data)
+    return output_csv
+
+
+if __name__ == "__main__":
+    scrape()