Portfolio Rubric Kit

Score your projects like a hiring manager. Upgrade any portfolio project in 30–60 minutes.

Most data science portfolios are a list of notebooks. Hiring managers look for evidence of real-world problem solving: clear problem framing, realistic data handling, rigorous evaluation, deployment thinking, and crisp communication.

The Portfolio Rubric Toolkit gives you the exact framework to evaluate and upgrade your projects with a structured scoring system and ready-to-use templates—so you can turn “nice project” into “interview-worthy case study.”

What you get (Downloadable Templates)

1) Data Science Portfolio Rubric & Scoring Sheet (XLSX)
A hiring-manager style scorecard to grade each project across core dimensions with clear scoring guidance and space for evidence links (README, notebook, demo).

2) Portfolio Self-Review Worksheet (PDF)
A fast checklist that tells you exactly what to add or fix. Any unchecked item becomes your next edit.

3) Gold Standard Portfolio Project (Annotated PDF)
A complete example project write-up, annotated to show what “excellent” looks like—so you can model your own portfolio after it.

4) Editable Portfolio Write-up Template (DOCX)
A clean structure for turning any project into a professional case study (problem → data → method → evaluation → deployment → communication).

Who this is for

• Intermediate to senior Data Scientists who want a portfolio that signals real-world readiness
• Candidates applying for product analytics, ML, experimentation, forecasting, or applied DS roles
• Anyone tired of portfolio advice that’s vague, generic, or purely Kaggle-focused

Why it works

This toolkit focuses on what hiring teams actually evaluate:

• Can you define the decision your work supports?
• Did you use realistic data and document assumptions?
• Is your evaluation trustworthy and aligned to business cost?
• Do you understand deployment, monitoring, and next steps?
• Can you communicate insights clearly and professionally?

How to use (3 steps)

1. Choose one portfolio project.
2. Score it using the rubric (5–10 minutes).
3. Use the worksheet to fill gaps and upgrade the project (30–60 minutes).

What you’ll be able to say after using this

• “Here is the decision this model supports and the metric that defines success.”
• “Here’s the baseline, the validation method, and what fails (with examples).”
• “Here’s how I would deploy and monitor it in production.”

Files included

• DS_Portfolio_Rubric_Scoring_Sheet.xlsx
• Portfolio_Self_Review_Worksheet.pdf
• Gold_Standard_Portfolio_Project_Annotated.pdf
• Portfolio_Project_Writeup_Template.docx

FAQ

Is this beginner-friendly?
Yes, but it’s designed to help you present work at an intermediate/senior standard.

Do I need to deploy models to use this?
No. The toolkit shows how to document deployment thinking even if you’re not an MLOps engineer.

Can I use this for analytics-only projects (no ML)?
Yes. The rubric covers decision framing, evaluation, and communication—valuable for analytics and experimentation too.