Resume Templates

Data & AI resume templates

Templates for work that is judged on a number: space for the baseline as well as the lift, and room for the tooling half of a data job.

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How data resumes get screened

Data and machine learning postings usually sit in the same systems as the rest of tech hiring, and the filter recruiters lean on hardest is the tool list. SQL, Python, dbt, Spark, Airflow, PyTorch, Snowflake, BigQuery: these are matched literally, so write them the way the posting writes them and drop the invented umbrella terms.

One caution specific to this field. Machine learning claimed on a resume that only demonstrates dashboards reads as a stretch to anyone technical, and a screening call finds it in the first five minutes. Claim the layer you actually worked at.

What does a strong data bullet actually report?

A baseline and a delta. Data work without a comparison point is unfalsifiable, and reviewers in this field are trained to notice that. Name the model or the query, name what it beat, and name what changed downstream as a result.

  • Replaced a rules-based churn flag with a gradient-boosted model, lifting precision from 0.41 to 0.68 at the same recall.
  • Rebuilt the nightly revenue pipeline in dbt; runtime fell from 4h10m to 38 minutes and three recurring reconciliation tickets stopped.
  • Cut warehouse spend 27% by partitioning the two largest event tables and retiring 60 unused scheduled queries.
  • Shipped a self-serve model that took 40% of ad-hoc SQL requests off the analytics queue.

The formatting habit that hurts data resumes

Notebook density. Analysts and scientists often try to reproduce a report in resume form: small type, dense paragraphs, a table of tools with version numbers. It reads as unedited rather than thorough.

The other common one is listing a competition rank or a course certificate above paid work. A certificate is context, not experience, and leading with it signals you do not yet have enough of the latter. Certifications belong in a short block beside education.

Which Data & AI roles are covered?

Every job title below has templates in the gallery, each paired with a career stage so the section order already fits the application you are making. Open the gallery and filter to Data & AI to see them.

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Machine Learning Engineer
  • AI Research Scientist
  • Business Intelligence Analyst
  • Database Administrator
  • Analytics Manager

Browse all resume templates

Related industries

Job titles sit across boundaries more often than a filter admits. If your work spans more than one of these, the nearest two are worth reading as well.

  • Technology & Software resume templatesTemplates built for the way engineering resumes actually get read: a stack block near the top, a repo link that survives parsing, and shipped-work bullets that carry a number.
  • Science & Research resume templatesTemplates for the point where a CV has to become a resume: techniques and instruments up front, publications kept but selected, everything else cut to two pages.

Data & AI resume questions.

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Include it when the work behind it is genuinely yours and genuinely finished. A repository of half-run tutorial notebooks is worse than no link at all, because a technical reviewer will open it and read the commit history. What helps is two or three projects with a written README explaining the question, the data and the result. A hiring manager who reads that has already done half the screening they would otherwise do on a call. Put the URL as plain text in the contact line so it survives parsing and stays usable on a printed copy. If most of your strongest work sits behind an employer's firewall, say so in one line and describe the outcome instead. Nobody expects a proprietary pipeline to be public, and inventing a public substitute for it is not required. One well-documented project beats six abandoned ones, and reviewers in this field say so openly.

Separate what you built from what you used. Training and evaluating a model, choosing the loss, handling the class imbalance and shipping it behind an endpoint is machine learning engineering, and it should be described that way with the metric you moved. Calling a hosted model's API or running a pre-built AutoML job is real work too, but it is integration, and describing it as developing a deep learning model collapses under the first follow-up question. The safer framing is also the more impressive one: name the concrete decision you made and what it cost or saved. Reviewers in this field read hundreds of resumes claiming model ownership, and the ones that survive are specific about the part the candidate personally did rather than the part the team shipped. Where you inherited a model and improved it, say that too, because maintaining a model in production is a skill hiring teams complain they cannot find.

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