Situation
Recruitment teams process PDF, DOCX, multi-file, and ZIP resume batches, and need candidate evidence they can compare consistently, without losing source files, progress, or human control over hiring decisions.
Data Science
Recruitment ATS Pro
A recruitment data workflow that structures CV information, applies semantic matching, and brings decision support directly into Odoo.
View source repository↗
Why it matters
Turns scattered candidate documents into comparable talent evidence while keeping recruiters responsible for the final decision.
My role
Data scientist, NLP practitioner, and workflow-integration developer
- 01Mapped the recruitment data flow from multilingual PDF/DOCX/ZIP ingestion through extraction, embedding, FAISS ranking, comparison, shortlisting, and final Odoo applicant creation.
- 02Implemented local SentenceTransformers embeddings, serialized FAISS indexes with a NumPy fallback, and bidirectional matching from jobs to candidates and candidates to jobs.
- 03Moved extraction, indexing, and scoring into bounded OCA queue_job workers while preserving source evidence, recruiter control, privacy, access rules, and operational monitoring.
Task
Build a recruitment data workflow that structures CV evidence, ranks candidates against jobs with semantic matching, and integrates decision support directly into the Odoo hiring process recruiters already use.
Action
- PDF, DOCX, multi-file, folder, and ZIP imports are validated before an extraction cascade uses pdfplumber, PyMuPDF, and python-docx to recover searchable text, structuring name, email, phone, LinkedIn URL, and section metadata while the original document stays available for verification.
- A locally loaded SentenceTransformers model encodes CV and job-description chunks for English and French semantic retrieval, without sending personal data to an external AI API.
- L2-normalized vectors are indexed with FAISS IndexFlatIP for exact similarity search, serialized per job with a NumPy fallback, turning candidate evidence into inspectable matching scores rather than an unexplained ranking.
- OCA queue_job moves extraction, indexing, and scoring into a bounded ML channel with persistent queued, running, completed, failed, and retried states, so recruiter screens are never blocked.
Result
Recruiters can search, filter, compare two to four profiles, inspect original CV passages, and shortlist candidates. Scores provide decision support while recruiters retain the final choice, and selected profiles move into native Odoo Recruitment stages instead of replacing the established process.
Technical implementation
How the solution was built.
Each layer connects an implementation choice to the decision or workflow it supports.
06 layers| Layer | Implementation | Operational purpose |
|---|---|---|
| Document ingestion & OCR | PDF, DOCX, multi-file and ZIP validation; pdfplumber, PyMuPDF and python-docx extraction; OCR fallback for scanned or image-based CVs | Convert heterogeneous documents into traceable, searchable candidate evidence, including files without a usable text layer |
| Multilingual representation | Local SentenceTransformers embeddings for English and French CV and job-description chunks | Capture semantic fit beyond exact keyword matching without exporting personal data |
| Retrieval and ranking | L2-normalized vectors, FAISS IndexFlatIP exact search and a NumPy fallback | Return fast, reproducible candidate-job rankings with inspectable source passages |
| ML processing | Bounded OCA queue_job workers with persistent queued, running, failed and retry states | Keep extraction, indexing and scoring reliable without blocking recruiter screens |
| Business workflow | Native Odoo recruitment stages, applicant records, role-based access and recruiter validation | Put decision support inside the operational process while preserving human control |
| Monitoring and analytics | Matching views, operational monitoring and Power BI recruitment indicators | Track pipeline progress, stage conversion and profiles requiring action |
Product walkthrough
Screens connected to decisions.
dashboard
Gives recruiters a fast operational view of candidate volume, matching progress, and shortlisted profiles.
core recruiter workspace
Brings job pipelines, candidate activity, and recruiter tools together in one working view.
job to candidates
Ranks candidates against a single job posting using inspectable semantic similarity scores.
candidate profile
Structures extracted CV evidence into a profile recruiters can inspect before taking action.
candidate review
Lets a recruiter inspect a candidate's evidence, annotate findings, and record a screening decision.
comparison
Places candidate evidence side by side to support a more consistent shortlist decision.
cv to jobs
Ranks open roles against a single candidate's profile for candidate-to-job matching.
import existing job
Imports an existing Odoo job posting into the matching workflow without re-entering requirements.
import new job
Captures a new job's requirements before it enters the CV-matching and scoring pipeline.
matching priorities
Lets recruiters tune which requirements matter most, adjusting how candidates are scored and ranked.
odoo applicant
Shows a shortlisted candidate inside the native Odoo Recruitment applicant record.
administration
Controls access, roles, and system settings for the recruitment workflow.
privacy retention
Manages data-retention rules and candidate privacy settings for stored CV evidence.
scoring monitor
Tracks the status of extraction, embedding, and scoring jobs running through the ML queue.