I think some of the more fundamental statistics papers may have ground to stand on here in the top 20 if not top 10 in terms of total impact.

While some were not necessarily paradigm shifters they form the foundation of ML and DL inference.

Examples:

The positive false discovery rate: a Bayesian interpretation and the q-value: John Storey

Cox's Regression Model for Counting Processes: A Large Sample Study-P. K. Andersen, R. D. Gill

Nonparametric Estimation from Incomplete Observations: Kaplan and Meier

An Algorithm for Least Squares Estimation of Non-linear parameters-Marquardt

Maximum Likelihood From Incomplete Data Via the EM Algorithm-Dempster et al.