• Multifaceted Computational Scientist and Developer with 5 years of experience (1 in corporate database environment, 4 in computational research) in analyzing, scrubbing, and managing large datasets to solve complex…
• Multifaceted Computational Scientist and Developer with 5 years of experience (1 in corporate database
environment, 4 in computational research) in analyzing, scrubbing, and managing large datasets to solve
complex problems
• Authored a series of programs that analyzed over 200 million data entries of hourly temperature and
humidity data from NOAA’s public database
• Skilled in using programming languages like Python and C++ to develop custom solutions and analyze data
for precise quantitative results
• Proven ability to produce and present high-quality data visualizations to diverse audiences
• Experienced in managing data accuracy and integrity by meticulously organizing and maintaining
databases
• Possesses a strong background in science and mathematics, which was essential for developing and
testing a variety of models
Developed an NLP sentiment analysis program for use as an independent data scientist to provide quantitative analysis for qualitative inputs. Have employed it in professional contracts to analyze customer reviews and to provide comparison of contracting partners based on notes.
Created a total cost of ownership model for a new truck a shipping company was considering switching to. Created a model with a variable final cost based on technical inputs, fuel consumption, and uncertainty of use.
Established a predictive modeling simulation for how extreme temperature affects fuel consumption/maintenance schedules for trucks used by a shipping company. Linked typical weather trends to a company’s past shipping schedules to make predictions about future costs and utilized Uncertainty Analysis to make recommendations about vehicle rotation, preemptive maintenance, and other cost reducing measures.
Developed a robust ETL pipeline for converting long-term hourly weather data NOAA’s LCD into daily maximum/average quantities useful for public health inquries. The program imports hourly readings of weather, makes corrections, and outputs daily maximum/average readings for heat related quantities. It also creates probability distribution plots for those values for each city, and consolidates statistical information for each one of those cities into one large database, allowing for easy comparisons of extreme heat behavior for cities across the United States or even the world.
Engineered data consolidation solutions for over 10 small businesses, transforming scattered financial, inventory, and client records into comprehensive Excel dashboards; Developed a Python framework for a comparative NLP sentiment analysis tool to process reviews and qualitative records and deliver recommendations; Advised on optimal decision-making using scientific and analytical methods.
Used Python to create an ETL pipeline capable of analyzing over 200 million datapoints from multidecadal weather datasets and returning precise quantitative values relevant to scientific and public health inquiries; Developed specialized scripts, databases, and dashboards in Python and SQL as requested by project members; Presented progress in weekly meetings with experts to contribute to public health research.
Modified and wrote SQL queries to resolve technical issues for clients utilizing a specialized database-as-a-service platform; Communicated with clients to diagnose software problems and explain technical solutions in non-technical terms; Used Microsoft Azure to collaborate with engineering teams on large-scale projects and exchange critical system information.
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