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Data and AI resume examples
Resumes for analysts, engineers and scientists who work with data, from a first analyst job to running a data organization. Each one shows how to turn queries, pipelines and models into results a business reader understands.
- Entry-level data analystFirst analyst job: SQL, Power BI, a real project and an internship.Entry level
- Data analystMid-level analyst: experiments, metrics and decisions the business made.Mid-level
- Business intelligence analystPower BI, semantic models and reporting that a whole company trusts.Mid-level
- Data engineerPipelines, warehouses and streaming, with volumes, freshness and cost.Mid-level
- Data scientistModels and experiments in production, measured in business terms.Mid-level
- Senior data scientistLeading modeling work end to end, mentoring and setting standards.Senior
- Machine learning engineerTraining, serving and monitoring models in production, including LLMs.Senior
- Head of dataLeading analytics, data engineering and data science as one function.Director
Write about decisions, not dashboards
The most common weakness in data resumes is a list of artifacts: “built 30 dashboards”, “trained a random forest”, “wrote ETL pipelines”. A hiring manager wants to know what changed because of that work. Who used the dashboard, what did they decide, and what did it save or earn? “Built the inventory dashboard buyers now use to place weekly orders, cutting stockouts by 18%” answers all three.
When you cannot share revenue figures, use relative numbers (a 12% lift), volumes (40 million rows a day) or time saved (a 6-hour report down to 20 minutes). Approximate is fine; vague is not.
Name the stack where you used it
Data job ads screen hard on tools: SQL, Python, a BI platform, a warehouse (Snowflake, BigQuery, Redshift, Databricks), an orchestrator, a cloud. Keep a short grouped skills list for the applicant tracking system, then make sure the important tools appear again inside the bullets that prove you used them.
Be honest about depth. Listing PyTorch because you finished a tutorial invites an interview question you cannot answer. Interviewers for data roles often run live SQL or a take-home on exactly what your skills section claims.
Where AI and LLM work fits
Generative AI work belongs on a resume when it shipped and was measured: a retrieval system with an evaluation set, a classifier that replaced manual review, an assistant with adoption numbers. Prompt experiments and certificates of completion carry little weight on their own. Describe the problem, the evaluation and the result, the same way you would for any other model.
Questions and answers
Do I need a portfolio for a data job?
For first jobs and career changes, yes: one or two projects on real, messy public data with a short write-up do more than any certificate. Once you have two or more years of work experience, your bullets carry the weight and a portfolio is optional.
Is a master’s degree required for data science?
Not required, but common. Many data scientist roles ask for a quantitative master’s or equivalent experience. Analysts and data engineers are hired from bachelor’s degrees, bootcamps and other careers all the time.
Should I list Kaggle competitions?
A strong finish (a medal, a top few percent) is worth a line under Projects or Awards early in your career. Participation alone is not. After a few years of work, drop it unless you are a Kaggle Master or above.
How technical should my summary be?
Name your main tools and the kind of problems you solve, then one result. The reader is often a recruiter who matches keywords first and a hiring manager who reads for judgment second; write for both.