Platform Overview

Overview

The Professional CV Builder is a structured analytical platform that enables recruiters and professionals to create, manage, and analyze curriculum vitae with market intelligence capabilities. The system integrates personal data entry with advanced market cross-reference analytics to provide quantitative insights into skill alignment and readiness metrics.

Core Modules

Home

Landing page featuring profile selection (QA Analyst, QA Engineer, Data Scientist) and Technical Evidence Hub. Provides quick access to video demonstrations and analytical modules for each professional profile.

Data Entry

Comprehensive data collection interface for personal information, professional experience, education, certifications, skills, and projects. Supports profile-based presets to streamline data entry for QA and Data Science professionals.

Data Insights

Scope: Currently available for Data Scientist profile only.
Functionality: Market cross-reference analytics engine (v1) that compares CV skills against public datasets (Kaggle and O*NET) to generate readiness scores, coverage analysis, and dataset-specific alignment metrics. Provides three interactive Chart.js visualizations:

Chart 1 (Donut): Readiness score KPI centered inside donut chart
Chart 2 (Vertical Bar): Market demand vs present skills vs coverage gaps
Chart 3 (Grouped Bar): Kaggle and O*NET dataset alignment (matched/missing skills)

Admin

Management interface for viewing, editing, and deleting CV records. Provides access to all stored personal data, professional history, and CV generation capabilities (PDF export).

Methodology

Market Cross-Reference Model v1

The Data Insights module employs a quantitative skill alignment algorithm that normalizes and compares CV skills against market-validated datasets. The core readiness metric is calculated as:

Readiness Score = (Present Skills / Market Demand) × 100

Dataset Sources:

Kaggle Roles & Skills: Industry-validated competencies from data science job postings
O*NET Skills Database: Standardized occupational skill requirements

Skills are normalized to lowercase tags for consistent matching. Dataset-specific coverage analysis measures alignment independently for each source to identify dataset-specific gaps and strengths.

Design Principles

Recruiter-Centric

Interface optimized for professional recruiters with clean layouts and quick-access navigation.

Data-Driven Insights

Quantitative analytics backed by industry-standard datasets (Kaggle, O*NET) for objective skill assessment.

Performance Optimized

Flask backend with SQLAlchemy ORM, Bootstrap 5 frontend, and Chart.js for responsive visualizations.