Startup Scoring and Investment Intelligence Framework
A sector-based scoring framework to assess early-stage startups using parameters like market size, traction, defensibility, and team strength.
Overview
This project brings structure and data science to startup evaluation—helping investors efficiently identify promising opportunities.
Objectives
- Design a quantifiable model to score and rank startups across key sectors
- Streamline data collection and enrichment for company profiles
- Support investor decision-making with actionable analytics
- Track trends across SaaS, E‑commerce, EdTech, and related sectors
Approach
Data Collection & Wrangling
- Collected data from Crunchbase, LinkedIn, patent databases, and news APIs
- Used Python (Pandas, BeautifulSoup) and SQL for cleaning and enrichment
- Created variables for traction, funding stage, and team composition
Framework Development
- Weighted parameters: Market Size (25%), Traction (30%), Defensibility (25%), Team Strength (20%)
- Automated scoring in Python to generate comparative insights
Insights & Business Impact
- Shortlisted 12 high‑potential startups from 100+ profiles for a corporate VC
- Accelerated deal pipeline by 30% through automated evaluation
- Identified trends in SaaS AI automation and gamified learning
Ongoing Data Management
- Maintained sector lists and updated scorecards daily
- Monitored industry signals to flag funding rounds or strategic pivots
Tech Stack
Python (Pandas, NumPy, BeautifulSoup) · SQL · Excel · Crunchbase API · LinkedIn Data · News Feeds
Outcome
Delivered a repeatable, data‑driven startup assessment model that enhanced investor decision‑making and streamlined deal sourcing workflows.