Illustrative example · fictional profile, real scoring rubric
Data scientist example
84
out of 100
Resume Readiness Score
Medium confidence · 3 repos · 3 READMEsAna, here's what we found
Ana, the churn model is the rare ML project that goes all the way to serving, and the paper implementations show depth the resume should mention.
Together they read as research literacy plus production instinct.
100
Project Quality
70
Technical Breadth
100
Documentation
57
Engineering Practices
90
Career Presentation
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Standout project
churn-radar
The one to lead your resume with. It checks every box a recruiter skims for.
Substantial codebaseWell documentedDeployment-ready (Docker or infrastructure code)Engineering practices visible (tests or CI)External adoption or collaboratorsActively maintained
Top repositories
feature-forge★★★★substantial · documented
paper-notes★★★substantial · documented
Read like a code review
+Your deployment experience is visible: containerized or infrastructure-defined projects read as production-minded.
+Most of your top projects are documented, which is rarer than it should be.
+Recent, maintained work: your most important repositories show current activity.
+Automated pipelines show up in your work, a strong engineering-practice signal.
→Presentation looks solid; the next lever is measurable impact statements in each README.
Strongest languages
Jupyter Notebook56%
Python44%
Detected stack
Frameworks
FastAPIPyTorch
Data & infrastructure
PostgreSQLDocker
CI/CD
GitHub Actions
Evidence used
Everything above is computed from this. Nothing else.
3repos analyzed
3READMEs
1Dockerfiles
2CI workflows
80stars
234 KBof code
What a recruiter probably sees
"An ML engineer who takes models past the notebook stage into served, tested systems."
Based on the public repositories analyzed, a recruiter is likely to infer an ML engineer who takes models past the notebook stage into served, tested systems. Making evaluation results visible in the READMEs is the clearest next step.
@gradient-descent-into-madness · analyzed Jul 20, 2026zelume.io/github-resume
Recruiter visibility
80%
of expected data-ml signals present · heuristic
Missing: Notebooks or experiments
Drafted from the evidence
Written only from the repository evidence. No invented numbers.
churn-radarBuilt a churn prediction system with PyTorch served through FastAPI, containerized with tests and CI
feature-forgeDeveloped a feature engineering pipeline on PostgreSQL with automated tests
Where it lands in the editor
Ana Petrov
github.com/gradient-descent-into-madness
Projects
- Built a churn prediction system with PyTorch served through FastAPI, containerized with tests and CI
- Developed a feature engineering pipeline on PostgreSQL with automated tests
Skills
Jupyter Notebook, Python, FastAPI, PyTorch, PostgreSQL, Docker, GitHub Actions
Experience
- Software Engineering Intern, Freshbooks (Summer 2025)
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