AI-Based Lead Scoring
1 day ago
Job description
Project OverviewWe are building a Revenue Intelligence MVP for real estate companies.
The system will:
- Import CRM data via CSV
- Store and normalize leads in PostgreSQL
- Calculate a rule-based Lead Score 0–100
- Classify leads A/B/C/D
- Provide explanation for each score
- Display prioritized call lists in a dashboard
- Allow sales agents to log outcomes feedback loop
It is a structured lead scoring and prioritization system.
We are starting with a clean MVP no ML yet, rule-based logic.
⸻
Tech Stack Preferred
Backend:
Python + FastAPI
Database:
PostgreSQL
Frontend:
React
Hosting:
open to suggestion Vercel + Railway / AWS / etc.
If you propose a better stack, explain why.
⸻
Scope of Work MVP
Database Design PostgreSQL
Create the following tables:
- leads
- lead_signals
- lead_scores
- lead_score_explanations
- lead_outcomes
- users
Deliverables:
- Schema
- Migration files
- Documentation
CSV Importer
Build:
- CSV upload endpoint
- Column mapping logic
- Validation
- Normalization dates, numbers, phone
- Error reporting
- Duplicate detection basic email/phone
After import:
- Leads saved into DB
- Signals saved into DB
Scoring Engine Rule-Based
Create a modular scoring service that:
- Reads lead + signal data
Calculates:
- Dimension scores
- Total score 0–100
- Conflict deductions
- Assigns class A/B/C/D
- Generates explanation JSON
- Stores results in DB
Important:
- Scoring must be versioned
- Deterministic same input results in same output
⸻
REST API
Expose endpoints:
Leads
- GET /leads
- GET /leads
Scoring
- POST /leads/id/score
- POST /score/recompute-all
- POST /leads/id/outcomes
- GET /leads
Return structured JSON.
⸻
Dashboard Frontend
Create a clean internal dashboard with:
Lead List View
- Filter by class
- Filter by score
- Sort by score
- Show top reason
- Score display
- Score breakdown
- Explanation box
- Copilot suggestion text
- Feedback form
No complex animations needed.
⸻
Milestones Payment Protection
We will release payment per completed milestone after code review and testing.
⸻
Milestone 1 — Database + CSV Import 25%
Deliver:
- PostgreSQL schema
- CSV importer
- Validation
- Leads correctly stored in DB
- Test import with 500+ rows
Payment released only after:
- Successful test import
- No critical data loss
- Code review approved
Milestone 2 — Scoring Engine 25%
Deliver:
- Scoring logic implemented
- Breakdown calculation
- Class assignment
- Versioning
- Scores stored in DB
Payment released after:
- 20 sample leads tested
- Deterministic results verified
- Clean documentation
Milestone 3 —
REST API Layer 20%
Deliver:
- Working endpoints
- Lead detail returns score + breakdown
- Feedback endpoint functional
Payment released after:
- Postman test collection verified
- API documentation provided
Milestone 4 — Dashboard MVP 20%
Deliver:
- Lead list with filters
- Lead detail page
- Score + explanation display
- Feedback form
Payment released after:
- End-to-end test successful
- Leads visible + score displayed
- Feedback stored in DB
Milestone 5 — Deployment + Documentation 10%
Deliver:
- Production deployment
- Environment setup guide
- Readme documentation
- Setup instructions
Payment released after:
- Working hosted version
- Clean setup process
What We Expect From You
- Strong backend architecture experience
- Clean, modular code
- Clear communication
- Ability to work milestone-based
- Suggestions for improvement are welcome
Important
To ensure you read the full description, start your proposal with:
"I understand the modular scoring architecture."
Applications without this will be ignored.
Contract duration of more than 6 months.
Mandatory skills:
React, Python, PostgreSQL, PostgreSQL Programming, , API, Web Application
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