Why does NumPy load() take longer in a larger script than in another?

I scan for a trigger and when I get it, load a .npy file and process it. It started to take almost 2 seconds to load the NumPy file from within the process but when I tried to load the same file from a different script it takes 0.015 seconds.

How can I diagnose this? It used to be instantaneous originally and I am not sure why it started to take longer since 3 weeks ago. The production script uses multiprocessing. I created a module containing several definitions and I call one of those definitions in the multiprocessing script:

from projectAI.projectAI import dataProcessor
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initializations
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processes = []
process1 = Process(target = dataProcessor,
                   args   = (genModelName, dataPath, DB, Max, Min),
                   daemon = True)
processes.append(process1)

for i in range(Count):
    process = Process(target = appEstimator,
                      args   = (Data, str(i+1), city),
                      daemon = True)
processes.append(process)

for process in processes:
    process.start()

for process in processes:
    process.join()

The definition "dataProcessor" within the module "projectAI":

dataProcessor(genModelName, dataPath, DB, Max, Min):

    while True:

        # Check for a new entry in DB using queries

        if newEntry:
            data = np.load(dataPath + newEntry["fileName"] + ".npy")

            # Further processing

This has been in production for months and never had issues. Suddenly it started to take 1.8 to 2 seconds to load this file. I executed the following script in the same environment:

import numpy as np, time
T1 = time.time()
data = np.load(dataPath + newEntry["fileName"] + ".npy")
T2 = time.time()
print(T2-T1)

Which takes only 0.015 seconds. What might be the cause?