Taylor Reiter Portfolio
PhD computational biologist turned Data Scientist at Arcadia Science; portfolio links directly to published metagenomics research, GitHub analysis pipelines, and a blog documenting real methods development — not toy projects.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Research-grade reproducibility as the differentiator
Her linked GitHub repos pair every analysis with the paper it supports, Snakemake/conda pipelines, and rendered notebooks — the exact reproducibility bar biotech and research-adjacent employers screen for, versus disconnected Kaggle notebooks.
Domain specialization signals hire-ready expertise
Rather than generic "data scientist" framing, she anchors everything in metagenomics and sequencing data — a narrow domain where hiring managers can immediately map her work to open genomics/biotech roles.
Career transition documented, not hidden
The site openly traces her path from PhD researcher to industry Data Scientist, showing how academic technical depth converts into an industry title without restarting from zero.
What Data Scientists can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Pair every analysis repo with the publication or write-up it supports — reviewers trust an analysis more when they can check it against a real result.
If moving from academia to industry, connect your PhD-era skills (statistics, experimental design, pipelines) explicitly to the industry title you want.
Use pipeline tools (Snakemake, Nextflow, conda environments) even for personal projects — it signals you can hand off reproducible work.
Link outward: your portfolio does not need to hold every project natively if it clearly indexes GitHub, Scholar, and lab pages in one place.
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