Rachael Tatman Portfolio
Linguistics PhD and Kaggle Grandmaster whose site and notebooks focus on NLP taught through real, worked examples — "making NLP boring," i.e. reliable and reproducible rather than hype-driven.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Cross-disciplinary framing (linguistics + data science) as a differentiator
Her PhD in computational sociolinguistics gives her NLP work domain grounding most self-taught NLP portfolios lack.
Teaching-first notebooks build a public trail of expertise
Kaggle notebooks written explicitly as tutorials accumulate upvotes and comments over years, creating durable, third-party-validated proof of communication skill.
A stated point of view about the field
Positioning her work around a specific philosophy gives her a memorable identity beyond "does NLP" — hiring managers remember a coherent technical opinion.
What Data Scientists can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
If you have a non-CS academic background, use it as a specialization angle rather than downplaying it.
Write Kaggle notebooks as tutorials, not just submissions — explain the "why" behind each step.
Develop a one-line point of view about your specialty and repeat it consistently across bio, talks, and notebooks.
List workshops and teaching sessions you have run as evidence of communication skill.
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