  We propose a novel analysis strategy, that leverages the unique capabilities
of the DUNE experiment, to study tau neutrinos. We integrate collider physics
ideas, such as jet clustering algorithms in combination with machine learning
techniques, into neutrino measurements. Through the construction of a set of
observables and kinematic cuts, we obtain a superior discrimination of the
signal ($S$) over the background ($B$). In a single year, using the nominal
neutrino beam mode, DUNE may achieve $S/\sqrt{B}$ of $3.3$ and $2.3$ for the
hadronic and leptonic decay channels of the tau respectively. Operating in the
tau-optimized beam mode would increase $S/\sqrt{B}$ to $8.8$ and $11$ for each
of these channels. We premier the use of the analysis software Rivet, a tool
ubiquitously used by the LHC experiments, in neutrino physics. For wider
accessibility, we provide our analysis code.
