Overview
Linguistics graduates occupy a valuable niche in the research job market. Their skills span phonetics, syntax, semantics, corpus analysis, and computational methods, all of which are in demand at universities, language technology companies, and research organisations. The challenge is presenting these technical skills on a resume in a way that non linguists can understand.
This resume belongs to Esme, a Linguistics BA graduate from the University of Edinburgh who completed a summer research placement at the Phonetics Laboratory and worked as a research assistant on a corpus linguistics project. Her resume works because it quantifies her research contributions: 4,500 audio samples annotated, 80,000 tokens coded in a corpus, and 45 participants tested in a phonetics experiment.
Phonetics and experimental methods
If you have conducted phonetic experiments, describe the methodology, participant count, and instruments. Esme's resume mentions recording and annotating speech data from 45 participants using Praat, conducting acoustic analysis of 4,500 vowel tokens, and measuring formant frequencies across 3 dialect groups.
Name the software: Praat, ELAN, Audacity, SpeechRecorder. These are the tools that phonetics labs use, and listing them tells a PI you can start contributing immediately.
Corpus linguistics and annotation
Corpus work involves large scale data processing. If you annotated, tagged, or coded corpus data, state the corpus name, the number of tokens or texts, and the annotation scheme. Esme coded 80,000 tokens in the International Corpus of English (ICE-GB) for syntactic features using the ICE annotation scheme.
For computational linguistics roles, also mention any work with NLTK, spaCy, or other NLP libraries.
Fieldwork and language documentation
If you conducted linguistic fieldwork (recording speakers of under-documented languages, collecting dialect data, running sociolinguistic interviews), describe it with specific participant counts and recording hours. This kind of experience is especially valued for roles at institutions like SOAS, the Endangered Languages Archive, or the British Library Sound Archive.
Statistical analysis
Modern linguistics is quantitative. If you used R, SPSS, or Python for statistical analysis of linguistic data, specify the types of analyses: mixed effects models, chi-square tests, logistic regression, or multidimensional scaling. Esme lists R with lme4 and ggplot2, which signals she can handle the statistical demands of experimental linguistics research.
