Experience

DaVinci AI LLC

• Data Scientist

  • Jun 2020 - Present

  * Participated in defining use cases in relation to the client. Utilized data wrangling tools like SQL and pandas for data transformation. * Used Python to scrape, clean, and perform statistical analysis on large datasets. * Built iDiagnosis using a transfer learning algorithm, VGG-19, to diagnose and classify diseases.* Leveraged tweets to develop sentiment analysis models that helped improve sales and marketing strategies for clients. * Participated in defining use cases in relation to the client. Utilized data wrangling tools like SQL and pandas for data transformation. * Used Python to scrape, clean, and perform statistical analysis on large datasets. * Built iDiagnosis using a transfer learning algorithm, VGG-19, to diagnose and classify diseases.* Leveraged tweets to develop sentiment analysis models that helped improve sales and marketing strategies for clients.



Calabar Kitchen

• United States
• Information Services
• 1 - 100 Employee

• Data Analyst

  • Apr 2018 - Feb 2020

  * Analyzed old information architectures and contributed to the design and development of the new ones. * Reduced payroll costs by analyzing customer traffic and offering staffing recommendations. * Forecasted inventory depletion and the outcomes of new menu introductions based on seasonal demands. * Performed A/B testing & post-promotion analysis on various Facebook, radio & tv advertisement campaigns. * Analyzed the food menu's pricing model and interpreted seasonal trends to ensure goals were met and exceeded.



National Science Foundation (NSF)

• United States
• Research Services
• 700 & Above Employee

• Research Analyst Intern

  • Jun 2015 - Dec 2015

  * Carried out various performance tests on Ultra-High Performance Concrete. * Visualized the strain patterns using the Digital Image Correlation system. * Calculated key structural properties of UHPC using the data extracted from the DIC system. * Ultimately rejected our null hypothesis which stated that there was no statistically significant difference between High-Performance Concrete and Ultra-High Performance Concrete. * Carried out various performance tests on Ultra-High Performance Concrete. * Visualized the strain patterns using the Digital Image Correlation system. * Calculated key structural properties of UHPC using the data extracted from the DIC system. * Ultimately rejected our null hypothesis which stated that there was no statistically significant difference between High-Performance Concrete and Ultra-High Performance Concrete.