Experience

Tomorrow.io

• United States
• Software Development
• 100 - 200 Employee

• Senior Data Platform Engineer

  • Sep 2020 - Present

  Tomorrow.io is the world’s leading Weather Intelligence Platform™. Fully customizable to any industry impacted by the weather, customers around the world including Uber, Delta, Ford, National Grid and more use Tomorrow.io to dramatically improve operational efficiency. Tomorrow.io was built from the ground up to help teams predict the business impact of weather, streamline team communication and action plans, improve productivity, and optimize profit margins.Space: In case you have not heard, we are also going to space with our Operation Tomorrow Space initiative. We are building the first-of-its-kind proprietary satellites equipped with radar, and launching them into space to improve weather forecasting technology for everyone on Earth.



Amazon

• United States
• Software Development
• 700 & Above Employee

• Data Engineer

  • Oct 2019 - Sep 2020



Facebook

• Software Development
• 700 & Above Employee

• Data Engineer

  • Oct 2017 - Sep 2019

  • Designed and built data models to drive decision making for multiple advertising products • Built and optimized pipelines using Python, Hive, Spark, and Presto• Contributed to a Python framework to automate advertiser growth accounting and cohortretention analysis• Sought out discrepancies, inefficiencies, and failures in pipelines and datasets, identified their root causes, and fixed them



Sovrn

• United States
• Technology, Information and Internet
• 200 - 300 Employee

• Data Scientist

  • May 2017 - Sep 2017

• Data Analyst

  • Mar 2016 - May 2017



University of Hawaii at Manoa

• United States
• Higher Education
• 700 & Above Employee

• Research Assistant

  • Aug 2013 - Dec 2015

  • Analyzed remote sensing geospatial and observational data using various machine learning classification and regression techniques in order to investigate and predict the effects of aerosol emission on cloud effective radius• Worked with team of scientists and engineers to collect and analyze multiple terabytes of weather radar data, resulting in the publication of a research article in a highly respected academic journal• Excelled in graduate level courses in statistical analysis, information theory in machine learning,and satellite data applications• Delivered multiple conference and seminar presentations on applied machine learning in theatmospheric sciences



NASA Jet Propulsion Laboratory

• United States
• Defense and Space Manufacturing
• 700 & Above Employee

• Intern

  • Jun 2015 - Aug 2015

  • Used Python to rank the relative predictive importance of a number of atmospheric variables, leading to a better understanding of the influence of aerosol emission on cloud properties