This dissertation presents two chemo-dynamical analyses of metal-poor stars found within the Milky Way. 115 metal-poor candidate stars, including 28 confirmed very metal-poor stars, selected from the narrow-band Pristine photometric survey are presented based on CFHT high-resolution ESPaDOnS spectroscopy. An additional 30 confirmed very metal-poor stars selected from Pristine are examined based on Gemini/GRACES spectroscopy. Chemical abundances are determined for a total of 19 elements (Li, Na, Mg, K, Ca, Sc, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Y, Zr, Ba, La, Nd, Eu) across these studies, which are combined with Gaia DR2 parallaxes and proper motions to paint a chemically diverse map of ancient stars in the Galaxy. Abundance patterns similar to those seen in "normal" metal-poor Galactic halo stars are found in a majority of the stars studied here, however new discoveries of a handful of chemically unique and kinematically intriguing metal-poor stars are presented. The chemo-dynamics of these novel stellar relics point towards chemical signatures of unique and potentially unstudied stellar yields, in addition to stars with origins in accreted dwarf galaxies and the ancient progenitors of the proto-Milky Way. The success of these relatively small studies heralds the great contributions to Galactic archaeology expected from the next generation of large multi-object spectroscopic surveys. Contained within are two other projects that introduce data products related to Gemini Observatory instruments. A version of the convolutional neural network StarNet, tuned to medium-resolution R~6000 H-band spectra is presented. This model was trained on synthetic stellar spectra containing a range of data augmentation steps to more accurately reflect the observed spectra expected from medium-resolution instruments, like the Gemini-North Near-Infrared Integral Field Spectrometer (NIFS) or GIRMOS. In an era when spectroscopic surveys are capable of collecting spectra for hundreds of thousands of stars, fast and efficient analysis methods are required to maximize scientific impact, and StarNet delivers on these expectations over a range of spectral resolutions. Finally, a python package called Nifty4Gemini, and its associated Pyraf/Python based pipeline for processing NIFS observations is included. Nifty4Gemini reduces NIFS raw data and produces a flux and wavelength calibrated science cube with the full signal-to-noise, ready for science analysis. / Graduate