Aarav Mehta
L2 cluster · Python · APIs · SQL
92%
Strong evidence in Django APIs, async service work, and production Postgres ownership.
recruiter pilot · myro for hiring teams
Shortlist by skill evidence, not just by CV polish.
Paste a JD, keep the hiring decision on L2-cluster skills only, and review candidates through the same skill graph Myro already uses on the seeker side.
JD → L2 clusterRanked shortlistEvidence from CV3–4 profile handoff
- Same graph
- L1–L5 taxonomy
- Output
- 3–4 strong profiles
- Decision style
- one-click shortlist
mirror dataset
Python backend role · shortlist preview
Ishita Rao
L2 cluster · Python · Data pipelines · AWS
88%
Good fit for analytics-heavy backend roles with clear ETL and deployment proof in the CV.
Rohan Shah
L2 cluster · Python · FastAPI · Docker
84%
Best when the role needs API delivery speed, container comfort, and hands-on build signal.
pilot workflow
Recruiter workflow
Start with one JD. Myro normalises the role, narrows the comparison to L2 skills, and returns a smaller, explainable slate instead of an opaque ATS dump.
- Capture JD, company, industry, role, and must-have skills in one post flow.
- Lock comparison to L2-cluster skills for apples-to-apples candidate ranking.
- Hand hiring teams a tighter shortlist with evidence lines, not only keyword counts.
workflow
Built to reduce hiring noise, not decorate it.
Post the JD once
Use the same mirrored fields as the seeker CV side: job description, company, industry, role, and skill requirements.
Review ranked talent
See only candidates inside the selected L2 cluster, along with match confidence and proof extracted from their CV data.
Move the top few forward
Share 3–4 strongest profiles into recruiter workflow, interviews, or agency handoff without re-sorting the pile manually.
product layers
Useful surfaces we can attach before the full dashboard lands.
Mirror dataset
The recruiter view reads the same structured skill graph the candidate side writes into, so the language stays identical on both ends.
Structured JD intake
A cleaner replacement for free-form requisition chaos: company, role, industry, JD, and required skills captured in one predictable schema.
L2-only homogeneity
Recruiters decide from L2 clusters first, which keeps comparison fair and reduces noisy cross-skill CV inflation.
Human-ready handoff
The output is not a black-box score. It is a recruiter-readable panel with evidence, strengths, and why each profile made the cut.
the mirror
A true mirror means both sides speak the same skill language.
The B2C side tells seekers how companies read them. The B2B side should tell recruiters which seekers are strongest for a role, using the exact same dataset and taxonomy.
- Candidate skills are attached to the same normalized taxonomy that recruiter-side filters read from.
- JD skill requirements become the recruiter-side mirror of CV skill extraction, not a separate scoring universe.
- Every shortlist row can show evidence because the system is reading mirrored CV structure, not only text search.
next step
Need the cream of the CV, not another pile?
This slice sets up the recruiter-side operating layer without rebuilding the platform. It gives Myro a credible HR-tech front door before the full dashboard lands.