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Laboratory for Atmospheric and Space Physics
Data Scientist
June 19, 2026 – Present
SPRITE_DRP
August 27, 2024 – April 22, 2025
SPRITE Data Reduction Pipeline and Sample Data Products
View ProjectSPRITE_GUI
January 7, 2022 – June 8, 2022
A python dashboard for real time monitoring of the SPRITE cubesat detector readout
View ProjectInterferon
June 21, 2021 – October 20, 2021
provides further reduction and analysis of VIRUS-P data products from Remedy basic reduction
View ProjectPlot_LRS2_Spectra
January 22, 2020 – January 22, 2020
A python notebook with a class to make paper quality plots of LRS2 spectra
View ProjectHETDEX_Pilot_Survey_OIII
January 17, 2020 – January 17, 2020
The notebooks that contain a majority of the analysis for the HETDEX Pilot Survey (HPS) OIII Emitter Study. This study was published in The Astrophysical Journal: https://doi.org/10.3847/1538-4357/ab3df7
View ProjectEmission_line_fitter_MCMC
August 2, 2018 – February 26, 2025
Fits emission lines from data cube of spectra using MCMC (using python's emcee)
View ProjectMetallicty_MCMC
July 24, 2018 – January 17, 2020
Calculates Metallicity using Maiolino et. al. 2008 or Curit et. al. 2017 strong line relations with MCMC
View ProjectPandas_tutorial
June 27, 2018 – October 5, 2018
A Jupyter notebook tutorial to get you started with Pandas (and some Astropy Tables) with astronomy examples
View ProjectVIRUS_characterization
November 15, 2016 – February 13, 2017
Characterization scripts for VIRUS data
View ProjectLRS2_reduction
November 14, 2016 – October 9, 2018
Reduction script and config files for LRS2 reduction
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated in astrophysics and scientific data analysis, primarily using Python and Jupyter Notebook. While this demonstrates deep expertise in a specific domain, it suggests a potentially narrow breadth of experience in diverse industry applications or different data science paradigms (e.g., machine learning engineering, big data platforms, A/B testing). The projects are all marked as 'personal', which might indicate a preference for individual contribution or a lack of documented team-based project experience. This specialization could be a strong fit for roles within scientific research organizations but might require adaptation for broader industry data science roles.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a focus on analytical and technical tasks, but collaboration, communication, and problem-solving approaches are not detailed.