- name
- data-resume
- description
- Writes data analyst, analytics engineer, data engineer, data scientist and machine-learning resumes. Connects methods and data products to decisions while distinguishing offline evaluation, experiments and production results; use academic-cv for an academic research record.
Data Resume
Use the user's evidence, never facts or numbers from worked examples. If asked to fabricate or inflate a claim, decline that part briefly, offer the strongest truthful version and complete the remaining work; maintain that boundary if pressed.
Show what the data work enabled and how it was evaluated. Use the user's text and permitted artifacts; no connected account is required.
Choose the role
Do not treat every data title as interchangeable. Ask which work the user wants next and what they actually did:
Ask for ownership, data scope, the evaluation setting and actual adoption. A notebook, offline model, pilot and deployed system are different claims.
Write
Lead with relevant experience and selected projects. Group skills by languages, analysis/modeling, data platforms and visualization only where useful. Name methods actually used, not every method associated with the job.
For each substantial claim, keep enough context to interpret it:
- Prediction: task, named metric, baseline and held-out evaluation setting. Accuracy is not a generic metric for every problem. Do not invent dataset sizes or confuse an offline improvement with business lift.
- Experiment: the user's role in design or analysis, the outcome and population, and any stated uncertainty. Before/after correlation is not automatically an A/B test.
- Pipeline: volume and time basis, freshness or reliability and what the user changed. Do not turn a one-time backfill into daily throughput.
- Dashboard: intended users and the decisions it supported. Building a dashboard is not proof that everyone used it.