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Trey Briggs

Data Scientist

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Intro
Columbus, United States
Lab Analyst IV at Abbott
Studied Data Science at University of Virginia
Studied Biology at University of Virginia
Food & Beverages
Joined June 13, 2023

Skills

About
I am a recent graduate with a Master's degree in Data Science, equipped with a passion for extracting insights from complex data sets. With a strong foundation in statistical analysis, machine learning, and programming, I possess the skills necessary to tackle real-world challenges. Throughout my academic journey, I have honed my ability to transform raw data into meaningful and actionable information, aiding in strategic decision-making processes. As a meticulous problem solver and a lifelong learner, I am eager to apply my expertise in data science to make a valuable impact in the field and contribute to data-driven innovation.
Experience
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Abbott
Nov 2021 – Present
Columbus, OH
Lab Analyst IV
Prepare, analyze, and process nutrient testing on research and finished product samples. Compare results to predetermined specifications to ensure analytical records are complete, accurate and in compliance. Maintain good laboratory practices. Perform accurate and precise testing according to laboratory methods. Responsible for keeping up to date with sample due dates and sample backlogs. Operate various types of analytical instrumentation, including HPLC, UPLC and LC/MS. Conduct investigations into method deviations and out of specifications results. Ensure compliance, overall data integrity, and adherence to SOPS, safety, and in-house regulations.
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Nasa
Jun 2022 – Dec 2022
Remote
Data Science Intern
This year’s team will complete the algorithm that converts PARASOL data to NASA’s PACE L1C format, and use PARASOL-MAPP to perform retrievals on real PARASOL data in the ACTIVATE region (off the East coast of Virginia to Bermuda). The team will then compare its aerosol products and PM2.5 to GRASP aerosol products and PM2.5 at daily, weekly, monthly, and seasonal scales. The project will also use the latest instrument uncertainty noise model for PARASOL to re-perform the simulated data aerosol retrievals, and recreate the scatter plot detailing the performance of each parameter. The team will incorporate collocated PARASOL polarimeter and lidar data taken from CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), a two-wavelength polarization lidar instrument aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite. Ultimately, the goal is to train a neural network forward model to include the reflectance from land surface types of interest, such as urban and desert surfaces, further expanding the neural network model’s usefulness for air quality and aerosol properties across the globe.
Education
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University of Virginia
Aug 2021 – Dec 2022
Master of Science , Data Science
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University of Virginia
Aug 2015 – May 2017
BA, Biology