Data Scientist & AI Engineer README Template
This template helps ML engineers and data scientists present research, experimentation, and applied AI work in a way that feels technical and trustworthy.
Want to see it live first? Open full preview → or scroll down to see the embedded preview below.
Designed for
- Specialized data scientist github profile readme showcasing notebooks, model evaluation benchmarks, and SQL analysis.
- High-signal ai engineer github profile template featuring PyTorch, Hugging Face spaces, and LLM fine-tuning pipelines.
- Researchers and ML developers presenting empirical impact rather than generic widget walls.
Included in the layout
- Python, PyTorch, SQL, and Jupyter-friendly sections.
- Visible metrics and project snapshots.
- Structured storytelling around impact and learning.
Live Preview & Raw Markdown
Illustrative Example Data: All names, star counts, commit metrics, and project descriptions shown in this template preview are illustrative placeholders. When copying this markdown, replace them with your own verifiable repositories and credentials.
JK
🧠 Dr. Jordan Kim — AI Research Engineer
ML Research Engineer · NLP · LLMs · Kaggle Grandmaster 🏆
Kaggle Grandmaster
Google Scholar
HuggingFace
🔬 Research Profile
researcher = {
"name": "Dr. Jordan Kim",
"kaggle_rank": "Grandmaster (#47 globally 🏆)",
"papers_published": 14,
"citations": 3_847,
"h_index": 18,
}
"name": "Dr. Jordan Kim",
"kaggle_rank": "Grandmaster (#47 globally 🏆)",
"papers_published": 14,
"citations": 3_847,
"h_index": 18,
}
14
Papers
3.8k
Citations
18
h-index
#47
Kaggle Rank
🛠️ Technical Toolkit
ML & Deep Learning
PyTorch
TensorFlow
HuggingFace
CUDA
Python
Jupyter
Data Engineering
Apache Spark
Apache Airflow
SQL
Pandas
dbt
📚 Selected Publications
ContextBench: Evaluating LLMs on Long-Context Reasoning
NeurIPS 2024
234 citations
GNN-Drug: Graph Attention Networks for Drug Discovery
Nature MI
1,204 citations
🏆 Kaggle Competition Results
| Competition | Result | Medal |
|---|---|---|
| Google – Sign Language Recognition | 1st / 2,742 | 🏅 Gold |
| LLM Science Exam | 1st / 2,147 | 🏅 Gold |
| BirdCLEF 2023 | 2nd / 1,856 | 🥈 Silver |
| Feedback Prize – ELL | 4th / 2,654 | 🥉 Bronze |
<!-- ========================================================
🤖 GitHub Profile README — Data Scientist & AI Engineer
Replace all placeholders with your own information!
======================================================== -->
# 🧠 Dr. Jordan Kim — AI Research Engineer
<p align="center">
<img src="https://readme-typing-svg.demolab.com?font=JetBrains+Mono&weight=700&size=20&pause=1000&color=7C3AED¢er=true&vCenter=true&width=600&lines=ML+Research+Engineer;Deep+Learning+Practitioner;NLP+%7C+Computer+Vision+%7C+LLMs;Kaggle+Grandmaster+🏆;Published+AI+Researcher" alt="Typing SVG" />
</p>
<p align="center">
<img src="https://img.shields.io/badge/Kaggle-Grandmaster-20BEFF?style=for-the-badge&logo=kaggle&logoColor=white" />
<img src="https://img.shields.io/badge/Google_Scholar-4285F4?style=for-the-badge&logo=google-scholar&logoColor=white" />
<img src="https://img.shields.io/badge/HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=black" />
<img src="https://komarev.com/ghpvc/?username=your-username&color=7C3AED&style=for-the-badge&label=PROFILE+VIEWS" />
</p>
---
## 🔬 Research Interests
```python
researcher = {
"name": "Dr. Jordan Kim",
"affiliation": "AI Research Lab @ [University/Company]",
"phd": "Computer Science — NLP Specialization (2021)",
"focus_areas": [
"Large Language Models (LLMs) & RLHF",
"Multimodal Learning (Vision + Language)",
"Efficient Deep Learning (Quantization, Pruning)",
"Graph Neural Networks for Drug Discovery",
"Causal Inference in ML Systems",
],
"kaggle_rank": "Grandmaster (#47 globally 🏆)",
"papers_published": 14,
"citations": 3_847,
"h_index": 18,
"open_datasets_released": 6,
}
```
---
## 🛠️ Technical Toolkit
### Machine Learning & Deep Learning
### Data Engineering & Analytics
### Experimentation & MLOps
---
## 📚 Selected Publications
| Year | Title | Venue | Citations |
|------|-------|-------|-----------|
| 2024 | [ContextBench: Evaluating LLMs on Long-Context Reasoning](https://arxiv.org/abs/2024.xxxxx) | NeurIPS 2024 | 234 |
| 2023 | [EfficientMamba: State Space Models at Scale](https://arxiv.org/abs/2023.xxxxx) | ICML 2023 | 891 |
| 2023 | [MultiModal-CLIP: Extending CLIP for Tabular Data](https://arxiv.org/abs/2023.xxxxx) | EMNLP 2023 | 412 |
| 2022 | [GNN-Drug: Graph Attention Networks for Drug Discovery](https://arxiv.org/abs/2022.xxxxx) | Nature MI | 1,204 |
---
## 🏆 Kaggle Competition Results
| Competition | Result | Medal |
|-------------|--------|-------|
| Google – Isolated Sign Language Recognition | 🥇 1st / 2,742 teams | 🏅 Gold |
| LLM Science Exam | 🥇 1st / 2,147 teams | 🏅 Gold |
| BirdCLEF 2023 | 🥈 2nd / 1,856 teams | 🥈 Silver |
| Feedback Prize – English Language Learning | 🥉 4th / 2,654 teams | 🥉 Bronze |
---
## 📊 GitHub & Research Stats
<p align="center">
<img height="180em" src="https://github-readme-stats.vercel.app/api?username=your-username&show_icons=true&theme=tokyonight&hide_border=true&bg_color=0d1117&title_color=7C3AED&icon_color=9D38BD" />
<img height="180em" src="https://github-readme-stats.vercel.app/api/top-langs/?username=your-username&layout=compact&langs_count=7&theme=tokyonight&hide_border=true&bg_color=0d1117&title_color=7C3AED" />
</p>
---
## 🤝 Let's Collaborate
Open to: **ML Research Collabs · Speaking Engagements · Kaggle Teams · Consulting**
<p align="center">
<a href="https://scholar.google.com/citations?user=your-id"><img src="https://img.shields.io/badge/Google_Scholar-4285F4?style=for-the-badge&logo=google-scholar&logoColor=white" alt="Google Scholar" /></a>
<a href="https://huggingface.co/your-username"><img src="https://img.shields.io/badge/HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=black" alt="HuggingFace" /></a>
<a href="https://kaggle.com/your-username"><img src="https://img.shields.io/badge/Kaggle-20BEFF?style=for-the-badge&logo=kaggle&logoColor=white" alt="Kaggle" /></a>
</p>
---
<p align="center"><i>🧠 From <a href="https://github.com/your-username">your-username</a> — Advancing AI, one commit at a time.</i></p>
Who Should Use This Template (And When NOT To Use It)
✅ Recommended For:
Data Scientists, Machine Learning Engineers, AI Researchers, and NLP/LLM Developers building PyTorch, TensorFlow, Scikit-learn, and Hugging Face pipelines.
❌ When NOT to Use This Template:
Do NOT use this template for traditional frontend web development or general sysadmin roles. Data and AI hiring managers look for reproducible Jupyter notebooks, model evaluation curves, and dataset provenance rather than web UI layouts.
How to Show Actual Machine Learning Depth Beyond Framework Lists
Hiring managers look for concrete technical signal rather than superficial decoration. Here are the core dimensions this template is engineered to communicate:
- Dataset Provenance & Preprocessing: Explain where your training data originated, how you cleaned edge cases, and handled class imbalance.
- Concrete Evaluation Metrics: State precision, recall, F1-scores, or BLEU/ROUGE benchmarks compared against baseline models, rather than claiming "99% accuracy" without context.
- Inference & Deployment Latency: Demonstrate deployment capability using FastAPI, ONNX Runtime, TensorRT, or Dockerized model serving.
- Interactive Notebooks & HuggingFace Spaces: Provide one-click links to Google Colab or HuggingFace demo spaces so technical reviewers can test your models immediately.
Section-by-Section Architecture Breakdown
01. Hero & Positioning Title
Clear role title and focus area. States your specialization immediately without forcing the reviewer to scroll.
02. Categorized Competencies
Groups skills into core focus domains. Prevents cognitive fatigue from giant unranked badge walls.
03. Featured Project Matrix
Replaces plain repo links with a structured table: Problem Context, Technical Architecture, and direct Clickable Proof.
What We Intentionally Left Out (And Why)
- No Vague "AI Enthusiast" Buzzwords: State your exact subfields (e.g. Computer Vision, Retrieval-Augmented Generation, Tabular ML) and architectures.
- No Unverified Accuracy Claims: Always pair performance claims with dataset context and evaluation splits.
- No Distracting Widget Walls: Keep focus strictly on research papers, Colab links, and model metrics.
💡 ReadmeDesign Design Philosophy
ReadmeDesign does not believe every profile needs every widget. Our templates include optional components; we strongly advise developers to keep only the elements that provide real, verifiable evidence of their technical work and prune everything else.
Bad vs. Good Profile Execution
❌ Common Pitfall (Default Profile)
Bio says "Passionate coder who loves coffee ☕", 35 Shields.io badges in a giant block, unlinked repositories with default descriptions, no architecture context, and broken visitor widgets.
✅ High-Signal Execution (ReadmeDesign Architecture)
Clear role positioning, 6 to 8 core categorized skills, 2 flagship projects with measurable outcomes, clean architecture summaries, and instant live demo or code links.
5-Step Customization Roadmap
- Step 1: Copy the template code using the button above and paste it into our Live Sandbox.
- Step 2: Replace the hero placeholder with your actual name, target role title, and elevator pitch.
- Step 3: Select your 2 strongest repositories. Add working preview URLs, documentation links, and measurable performance or throughput numbers.
- Step 4: Audit your Markdown in our README Analyzer against our 10-dimension rubric.
- Step 5: Commit to your
username/usernamerepository'sREADME.md.
Published by Shubham Sharma · Last reviewed for GitHub Markdown compatibility: 2026.
02. Tech Stack Shields
Standardized Shields.io badges enable instant visual categorization of languages, frameworks, and cloud tooling.
03. Featured Project Matrix
Replaces plain repo links with a 3-column table showing project purpose, tech stack, and direct live demo links.
Before vs. After Profile Impact
❌ Default / Unoptimized Profile
Blank repository README, generic bio ("I like building stuff"), missing live demo links, no contact info for recruiters.
✅ ReadmeDesign Optimized Profile
Clear role positioning, standardized tech badges, measurable project outcomes, live demo links, and active commit streak cards.
Recruiter & Engineering Lead FAQ
Q: How many pinned repositories should I include?
A: We recommend 2 to 4 high-quality repositories. Each repository should have a live demo link, a clean README file, and explicit technical details explaining your architectural choices.
Q: Do GitHub stats cards update automatically?
A: Yes! Dynamic widgets like
github-readme-stats fetch real-time data directly from the GitHub API every time someone views your profile.Q: How do I test my README before pushing live?
A: You can paste your Markdown into our Live README Sandbox or audit your profile with our README Analyzer Tool before committing.