Michele Tobias Portfolio
Geospatial Data Scientist at UC Davis whose portfolio documents a crowd-sourced GIS data project (American Viticultural Areas) end-to-end and separates teaching, code, and research into distinct sections.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Community/crowd-sourced data project as a leadership signal
Leading the AVA digitizing project shows she can coordinate distributed data collection and turn messy, crowd-sourced input into a maintained, structured dataset — beyond solo notebook work.
Separation of teaching, code, and research
Splitting the site into distinct sections lets a hiring manager jump straight to evidence relevant to the role, e.g. "Code & Projects" for an engineering-heavy screen.
Niche technical specialization (geospatial)
GIS is a specific, in-demand sub-specialty (logistics, agriculture, climate, urban planning) few generalist candidates can credibly claim — narrow expertise reads as depth.
What Data Scientists can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
If you lead or maintain a community/open dataset, document that leadership explicitly — it is a rarer signal than another solo project.
Organize a portfolio by function (teaching, code, research) rather than chronology so recruiters can navigate directly to what matters.
Own a geospatial or other domain niche if you have one — "GIS-focused data scientist" is more filterable than "data scientist."
Workshops and training materials count as portfolio evidence: they prove you can explain technical work to non-experts.
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