diff --git a/requirements.txt b/requirements.txt
index d28ae9f..1dee38a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -19,4 +19,6 @@ Requests==2.31.0
 setuptools==68.0.0
 textdistance==4.6.1
 usaddress==0.5.4
-networkx~=3.1
\ No newline at end of file
+names-dataset==3.1.0
+networkx~=3.1
+
diff --git a/utils/linkage.py b/utils/linkage.py
index 9884482..cae5024 100644
--- a/utils/linkage.py
+++ b/utils/linkage.py
@@ -1,3 +1,7 @@
+import textdistance as td
+import usaddress
+from names_dataset import NameDataset
+
 """
 Module for performing record linkage on state campaign finance dataset
 """
@@ -7,8 +11,6 @@
 
 import numpy as np
 import pandas as pd
-import textdistance as td
-import usaddress
 
 from utils.constants import COMPANY_TYPES, repo_root
 
@@ -397,6 +399,54 @@ def get_street_from_address_line_1(address_line_1: str) -> str:
     return " ".join(string)
 
 
+def name_rank(first_name: str, last_name: str) -> list:
+    """Returns a score for the rank of a given first name and last name
+    https://github.com/philipperemy/name-dataset
+    Args:
+        first_name: any string
+        last_name: any string
+    Returns:
+        name rank for first name and last names
+        1 is the most common name, only for names in the United States
+        First element in the list corresponds to the rank of the first name
+        Second element in the list corresponds to the rank of the last name
+        Empty or non string values will return None
+        Names that are not found in the dataset will return 0
+
+    >>> name_rank("John", "Smith")
+    [5, 7]
+    >>> name_rank("Adil", "Kassim")
+    [0, 7392]
+    >>> name_rank(None, 9)
+    [None, None]
+    """
+
+    # Initialize the NameDataset class
+    nd = NameDataset()
+
+    first_name_rank = 0
+    last_name_rank = 0
+    if isinstance(first_name, str):
+        first_name_result = nd.search(first_name)
+        if first_name_result and isinstance(first_name_result, dict):
+            first_name_data = first_name_result.get("first_name")
+            if first_name_data and "rank" in first_name_data:
+                first_name_rank = first_name_data["rank"].get(
+                    "United States", 0
+                )
+    else:
+        first_name_rank = None
+    if isinstance(last_name, str):
+        last_name_result = nd.search(last_name)
+        if last_name_result and isinstance(last_name_result, dict):
+            last_name_data = last_name_result.get("last_name")
+            if last_name_data and "rank" in last_name_data:
+                last_name_rank = last_name_data["rank"].get("United States", 0)
+    else:
+        last_name_rank = None
+    return [first_name_rank, last_name_rank]
+
+
 def convert_duplicates_to_dict(df: pd.DataFrame) -> None:
     """Saves to the "output" directory a file where each row represents a string
     matching to another string