Data annotator with 2 years labeling image, video and text data for autonomous driving and retail AI projects. Keep a 98.7% QA accuracy score while labeling 600–800 bounding boxes per shift. Follows complex guidelines closely and flags edge cases that improve them.
Experience
Data Annotator, Labelcrest Data Services (Remote)
Jan 2025 – Present
Label 600–800 bounding boxes, polygons and LiDAR cuboids per shift for driving datasets in CVAT and Labelbox
Hold a 98.7% QA accuracy score over 18 months, above the 97% team requirement
Flagged 45 ambiguous edge cases that led to 6 guideline updates for pedestrians and road signs
Review peer work as a second-pass QA reviewer 2 days a week
Inventory Associate, Harbor Point Retail (Tacoma, WA)
Jun 2023 – Dec 2024
Scanned and counted inventory for a 40,000-item store with 99% count accuracy
Processed 3 truck deliveries a week and updated product records
Education
A.A. General Studies, Tacoma Community College (Sep 2021 – Jun 2023)
Tools: CVAT, Labelbox, Label Studio, Google Sheets
Work Habits: guideline adherence, edge-case documentation, steady throughput
How to write a Data Annotator resume
A strong Data Annotator resume shows the questions you answered, the models or pipelines you shipped, and the decisions your analysis changed. Put your most relevant recent role first and make every bullet show a result, not a duty.
Sections, in order
Summary
Experience
Projects
Skills
Education
Length: One page for most candidates; move to two only if every line is relevant.
Summary: Write two or three lines: your role, your strongest skills, and one result that proves them.
Skills to feature
SQL
Python or R
Statistics
Machine learning
Data modeling
dbt and ELT pipelines
BI tools (Looker, Tableau, Power BI)
Experiment design
Data visualization
Stakeholder communication
Example bullet points
Built a churn model (gradient boosting, AUC 0.86) that targeted retention offers and saved $900k a year
Rebuilt the revenue dashboard on dbt and Looker, cutting refresh time from 6 hours to 20 minutes
Designed 25 A/B tests with sequential analysis, shortening the average test from 4 weeks to 10 days
Tips from recruiters
Pair every technique with the decision it informed; "built a model" alone says little.
List tools under grouped skills ("Analytics: SQL, dbt, Looker") so ATS keyword matches are exact.
Link a notebook, Kaggle profile or public analysis if you have one.
Recommended templates
Classic (ATS-friendly): Jake's-style LaTeX classic: small caps and hairline rules
Modern (ATS-friendly): Clean single column with accent headings and rules
Compact (ATS-friendly): Dense single column that fits long histories
Data Annotator resume FAQ
What should a Data Annotator resume include?
A Data Annotator resume should show the questions you answered, the models or pipelines you shipped, and the decisions your analysis changed. Use these sections in this order: Summary, Experience, Projects, Skills and Education. Put your most relevant recent role first and make every bullet show a result, not a duty.
What is the best resume template for a Data Annotator?
Classic, Modern and Compact work well for Data Annotator roles. They are single-column with standard headings, so applicant tracking systems (ATS) parse them reliably.
How long should a Data Annotator resume be?
One page for most candidates; move to two only if every line is relevant.
Which skills should a Data Annotator put on a resume?
Prioritize the skills named in the job posting. Common ones for Data Annotator roles are SQL, Python or R, Statistics, Machine learning, Data modeling and dbt and ELT pipelines.
How do I write strong Data Annotator resume bullet points?
Start with an action verb, say what you did, and end with a measurable result such as forecast accuracy, revenue impact and query time. For example: "Built a churn model (gradient boosting, AUC 0.86) that targeted retention offers and saved $900k a year."