I build reliable data frameworks and turn complex technical concepts into clear, structured workflows.
Backed by 7 years of managing structured academic operations and technical engineering curriculum, I specialize in data validation, operations quality control, and data modeling. I know exactly how to manage massive, high-stakes documentation pipelines where errors are not an option.
What I bring to your remote team:
- Data Infrastructure & Modeling: Engineering robust Excel and Google Sheets tracking models to prevent manual entries and formula breakage.
- Technical Data Processing: Writing Python and R scripts to clean, process, and validate large-scale data points, including geospatial analysis in Google Earth Engine.
- Operational Quality Auditing: Standardizing workflows to meet strict regulatory compliance guidelines, drawn from 7 years managing ISO 9001 documentation.
- Technical Translation: Explaining complex technical data systems to non-technical stakeholders or clients clearly and simply.
My Stack: Excel, Google Sheets, Python, R, Google Earth Engine, QGIS, ArcGIS.
If you need a data analyst who is deeply organized, processes records flawlessly, and requires zero hand-holding—let's talk.
Experience: 5 - 10 years
Experience: 5 - 10 years
Experience: 5 - 10 years
Experience: 5 - 10 years
Experience: Less than 6 months
Experience: 5 - 10 years
I've been doing data analysis in some form since my undergraduate thesis in 2016 - roughly 9-10 years, though the intensity has varied. During my BS thesis, I did early-stage data collection and validation work. Throughout my seven years as a university instructor (2017-2024), I worked with data at a light-to-moderate level - academic records, documentation, and structured reporting requiring accuracy and consistency. During my Master's (completed 2026), I moved into heavy, sustained analysis: cleaning and processing 25 years of satellite time-series data (11,320+ records across 270 sites), applying statistical methods and unsupervised machine learning (clustering, PCA) to find patterns invisible in raw summary numbers, and validating results through deliberate stress-testing. That thesis work is the deepest, most technical analysis I've done, and it's shaped how I approach every dataset since - clean it thoroughly, question the obvious answer, and validate before trusting a conclusion.
Experience: 1 - 2 years
Experience: Less than 6 months
Used extensively during Master's thesis for statistical analysis, data cleaning, and unsupervised machine learning models.
Experience: Less than 6 months
Experience: Less than 6 months
Applied for spatial data extraction, processing, and visualization across large-scale datasets during graduate research.
“My Filipino specialist who is absolutely amazing..go get your OFS today!”
Eden Einav
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