diff --git a/analytics/lib/fixtures.py b/analytics/lib/fixtures.py
index 12eed78d01..17168110c1 100644
--- a/analytics/lib/fixtures.py
+++ b/analytics/lib/fixtures.py
@@ -1,5 +1,5 @@
 from math import sqrt
-from random import gauss, random, seed
+from random import Random
 from typing import List
 
 from analytics.lib.counts import CountStat
@@ -36,6 +36,8 @@ def generate_time_series_data(
     partial_sum -- If True, return partial sum of the series.
     random_seed -- Seed for random number generator.
     """
+    rng = Random(random_seed)
+
     if frequency == CountStat.HOUR:
         length = days * 24
         seasonality = [non_business_hours_base] * 24 * 7
@@ -44,13 +46,13 @@ def generate_time_series_data(
                 seasonality[24 * day + hour] = business_hours_base
         holidays = []
         for i in range(days):
-            holidays.extend([random() < holiday_rate] * 24)
+            holidays.extend([rng.random() < holiday_rate] * 24)
     elif frequency == CountStat.DAY:
         length = days
         seasonality = [8 * business_hours_base + 16 * non_business_hours_base] * 5 + [
             24 * non_business_hours_base
         ] * 2
-        holidays = [random() < holiday_rate for i in range(days)]
+        holidays = [rng.random() < holiday_rate for i in range(days)]
     else:
         raise AssertionError(f"Unknown frequency: {frequency}")
     if length < 2:
@@ -62,11 +64,10 @@ def generate_time_series_data(
         seasonality[i % len(seasonality)] * (growth_base**i) for i in range(length)
     ]
 
-    seed(random_seed)
-    noise_scalars = [gauss(0, 1)]
+    noise_scalars = [rng.gauss(0, 1)]
     for i in range(1, length):
         noise_scalars.append(  # noqa: PERF401 # https://github.com/astral-sh/ruff/issues/6210
-            noise_scalars[-1] * autocorrelation + gauss(0, 1) * (1 - autocorrelation)
+            noise_scalars[-1] * autocorrelation + rng.gauss(0, 1) * (1 - autocorrelation)
         )
 
     values = [