- Description: Automatically parse resumes and match candidates to job openings using ML algorithms.
- Tech Stack: Python, NLP, Scikit-learn.
- Web Portal Utilization: Streamline the hiring process for recruiters.
Tasks:
- Create resume parsing logic using NLP (20 hrs).
- Develop a job-to-resume matching algorithm (16 hrs).
- Integrate the system with the recruiter portal (16 hrs).
- Add feedback loop to improve parsing accuracy (8 hrs).
Total Effort: ~60 hrs
Dependencies: Requires job postings and user profile data available.
To determine whether the Resume Parsing and Candidate Matching solution meets the requirement specifications. Below is a categorized list of comprehensive test cases to evaluate whether the solution meets expectations:
| Test Case | Objective | Expected Outcome (Exceeds Spec) |
|---|---|---|
| Parse resumes with mixed languages (e.g., English + Hindi) | Validate multilingual support | Successfully extract both language data where applicable |
| Parse resumes with poor formatting or OCR-based scanned documents | Handle edge-case input | Extract majority of key fields despite layout noise |
| Parse resumes with infographics and charts | Evaluate image parsing capability | Return contextual info from visual data using OCR/vision models |
| Extract soft skills and intent | Go beyond hard skills | System detects leadership, communication, etc. from descriptions |
| Extract GitHub, LinkedIn links | Enrich candidate profile | Parsed links are valid and clickable |
| Extract job titles and map to standard taxonomy | Normalize job titles | Correctly maps "SDE-2" to "Software Engineer – Mid-level" |
| Test Case | Objective | Expected Outcome (Exceeds Spec) |
|---|---|---|
| Match across similar but differently worded job descriptions | Robust semantic matching | Top 5 matches remain mostly consistent |
| Identify overqualified/underqualified candidates | Use context, not keyword match | Accurate down-ranking or tagging |
| Match based on inferred intent (e.g., open to relocation) | Predict behavioral fit | Candidates flagged with extra inferred features |
| Display skill gap analysis for each candidate | Show mismatches | Clearly highlight missing or partial match skills |
| Historical matching improvement | Learning from feedback | Precision improves over time via feedback loop |
| Explainability of ranking | Model transparency | Recruiters can see why candidate scored high/low |
| Test Case | Objective | Expected Outcome (Exceeds Spec) |
|---|---|---|
| Accessibility audit (WCAG 2.1 AA compliance) | Inclusive design | System passes accessibility scan (color contrast, alt tags, keyboard nav) |
| Upload 100+ resumes in bulk | Stress test | No UI lag, feedback messages prompt, processing continues smoothly |
| Compare multiple job matches for a single resume | Rich recruiter utility | Recruiter can toggle between job roles and see fit score per job |
| Download full match report as Excel/PDF | Useful data export | Report includes resume summary, match %, reasons for mismatch |
| View feedback analytics | Admin usability | Feedback trends visualized over time (e.g., model improvement chart) |
| Test Case | Objective | Expected Outcome (Exceeds Spec) |
|---|---|---|
| Submit recruiter feedback on parsing errors | Model learning | Corrections are used to retrain and improve parsing model accuracy |
| Submit feedback on top-3 matches | Learning to rank | Matching engine adjusts and improves ranking over time |
| Feedback system bias detection | Ethical AI | System detects if feedback is skewing matches unfairly (e.g., age, gender bias) |
| Re-train model on feedback set | Evaluate feedback impact | New model shows statistically significant accuracy gain |
| Test Case | Objective | Expected Outcome (Exceeds Spec) |
|---|---|---|
| Resume parsing under 2 seconds for 90% of cases | Speed benchmark | Average is < 2 sec even under load |
| System scales with 1000 concurrent users | Scalability | No crash or significant delay |
| Resume + JD similarity > 0.85 on gold-standard set | Model performance | Outperforms baseline models by +10% F1 or recall |
| Downtime < 1% monthly | Reliability | High availability confirmed via uptime monitor |
| Test Case | Objective | Expected Outcome (Exceeds Spec) |
|---|---|---|
| Compare with open-source matchers (e.g., Elasticsearch, JobLib) | Relative benchmark | Custom ML solution outperforms open-source baseline by accuracy or speed |
| Support for plug-in external scoring models (e.g., BERT, SBERT) | Extensibility | Architecture supports ML model swapping or hybrid logic |
| Model retrain API triggered by dataset changes | Continuous learning | Automatically re-trains and notifies admin with updated accuracy logs |