AriaScout scores every candidate against a requisition and ranks by predicted placement success. When it is wrong a customer notices, because a bad submission goes to their client. This work owns that judgement.
Ranking candidates by whether they will actually be placed, not by keyword overlap with a job description.
Deriving behavioural signals across a network of staffing firms — response patterns, submission frequency, rate outcomes — without exposing one customer’s records to another.
Reconciling what a resume says against what the same person reported elsewhere on the network.
Every score ships with a rationale a recruiter can read. A model we cannot explain is a model we cannot sell.
Evidence over credentials. Having done something close to this and being able to explain the decisions you made counts for more than the title you held.
You have shipped models into a product where the prediction had consequences, not into a notebook.
You are rigorous about evaluation — you know what your baseline was and why your metric was the right one.
You have dealt with feedback loops, selection bias or label scarcity in a live system.
You can explain a model decision to someone who does not care how it works.
Email careers@ariascout.com with the subject line Applied ML — matching and scoring. Include:
No cover letter needed. We reply either way.
This is an open application, not an active search. We read every one and come back to strong applicants when a role opens.
We will run AriaScout against a live req from your desk and show you exactly what comes back — the candidates, the scoring rationale and the screening transcripts.