SkillScope is an end to end intelligence system that ingests real job postings, extracts skills using NLP, builds semantic embeddings, discovers latent skill clusters, and generates interactive insights for career and hiring intelligence. The goal is to move beyond keyword counting and instead model how industries semantically express technical expectations supporting skill gap analysis, hiring trends, and personalized job fit scoring.
Author: Aash Shah
Email: aashshah.04@gmail.com
GitHub: aashshahh
LinkedIn: linkedin.com/in/aash-shah-ba002224b
Hiring signals evolve quickly, and traditional skill lists rarely keep up. SkillScope analyzes live job postings to uncover:
- What technical skills matter most for Data Scientist, ML Engineer, Data Analyst, and MLOps roles
- How industries differ in the skills they prioritize
- Which skill clusters naturally emerge in modern job descriptions
- How user skills compare to real-world expectations This system mirrors how real analytics teams operate: scrape → clean → extract → embed → cluster → analyze → visualize.
SkillScope is an end to end data analysis pipeline that starts with raw job postings and ends with clear insight into the tools, frameworks, and languages companies are asking for today. The workflow mirrors how real analytics teams operate: acquire data, clean it, extract meaningful signals, and present insights that support decision making.
Technology evolves fast. Course syllabi and generic skill lists usually lag behind what employers expect right now. SkillScope addresses that gap by grounding its insights in freshly scraped job data. The goal is simple: help learners, educators, and early-career professionals invest their time in the skills that are actually showing up in current job descriptions.
flowchart TD
A["**Job Scrapers**<br>Indeed · RemoteOK · Wellfound"]:::wide -->
B["**Data Cleaning**<br>Regex · Standardization · Deduping"]:::wide -->
C["**Skill Extraction**<br>NER · Pattern Rules · Ontology Mapping"]:::wide -->
D["**Embeddings Layer**<br>Sentence-BERT · MPNet"]:::wide -->
E["**ML Layer**<br>KMeans · PCA · Similarity Models"]:::wide -->
F["**Analytics Outputs**<br>Clusters · Heatmaps · Skill Networks · Job-Fit Score"]:::wide -->
G["**Interactive Dashboard**<br>Streamlit"]:::wide
Job postings carry implicit meaning beyond keywords. Embeddings reveal deeper industry-specific patterns: finance → SQL, Airflow, risk modeling tech → PyTorch, transformers analytics → dashboards, experimentation frameworks These patterns highlight real shifts in technical expectations across sectors.
Using SBERT + KMeans, SkillScope surfaces hidden groupings such as: • core ML competencies • data engineering pipelines • cloud ecosystems • applied modeling + analytics This creates a semantic skill graph instead of a flat keyword dictionary.
By averaging embeddings from job descriptions and comparing them with user skill embeddings, we compute cosine-similarity-based job-fit scores. This becomes the foundation for a personalized, intelligent resume recommender.
Early signals show a clear progression: junior → tool-centric (SQL, Excel, scikit-learn) mid-level → systems (Spark, Airflow, AWS) senior → architecture, leadership, strategic modeling SkillScope aims to quantify this trajectory at scale.
| Stage | Description | Key Tools |
|---|---|---|
| 1. Collection | Scraped live postings using RemoteOK’s public API and Playwright automation. | Python, Requests, Playwright |
| 2. Cleaning | Removed duplicates, standardized fields, normalized skill tags. | Pandas, NumPy |
| 3. Skill Extraction | Tokenized and filtered tags to isolate individual technical skills. | NLTK, regex |
| 4. Analysis & Visualization | Counted skill frequencies and plotted demand trends. | Matplotlib, Plotly |
| 5. Dashboard (Planned) | Interactive web interface for exploring skill demand by category and region. | Streamlit |
skillscope/
│
├── src/
│ ├── ingest/ # scrapers, API clients
│ ├── clean/ # cleaning + normalization
│ ├── nlp/ # tokenization, extraction, embeddings
│ ├── models/ # clustering, similarity, scoring
│ ├── utils/ # logging, configuration helpers
│ └── dashboard/ # Streamlit UI
│
├── data/
│ ├── raw/ # original scraped job postings
│ ├── interim/ # cleaned intermediate datasets
│ └── processed/ # embeddings, clusters, skill ontology
│
├── config/
│ ├── settings.yaml # scraping + NLP configs
│ └── skills.json # curated skill ontology
│
├── notebooks/ # EDA + exploratory pipelines
├── report/ # final project report/slides
├── visuals/ # diagrams, charts
│
├── requirements.txt
├── .gitignore
└── README.md
Python 3.11+
BeautifulSoup Playwright Requests
spaCy NLTK HuggingFace Transformers Sentence-BERT
scikit-learn (KMeans, PCA, Agglomerative, similarity models)
Streamlit Plotly
CSV / Parquet
YAML JSON