Lead Quality & Knowledge

TL;DR

Lead Quality & Knowledge: Own the end-to-end quality loop for Customer Care at scale, with an accent on QA standards, training systems, and knowledge remediation. Focus on building consistent quality across markets, governing the knowledge layer that feeds AI agents, and turning quality metrics into action to improve CSAT and deflection.

Location: Paris

Company

hirify.global is a finance workspace company for SMEs with banking at its core.

What you will do

• Set and calibrate quality standards by defining the QA framework, running calibration sessions, and ensuring consistent scoring across channels and geographies.
• Build the training system with structured learning paths, certification frameworks, and training materials defining “what good looks like”.
• Own knowledge management end-to-end by running the internal KMS and Help Center, including content prioritization, governance, and Tone of Voice alignment.
• Be the quality guardrail for AI by owning the knowledge layer feeding the AI bot and automated agents and converting quality reviews into structured feedback for the AI Lab roadmap.
• Turn quality data into action by tracking quality scores, time-to-proficiency, KB freshness, and deflection rates to improve CSAT and escalation metrics.

Requirements

• 7+ years of experience in Customer Ops or Support quality and training roles, ideally in a fast-moving SaaS or tech environment.
• Experience designing QA frameworks, including calibration grids and scoring systems from scratch.
• Ability to influence stakeholders (Product, Legal, team leads) using data and credibility without relying on hierarchy.
• Systems thinking to anticipate second-order effects (e.g., FAQ gaps after product changes, compliance edge cases in bot content).
• Comfort owning the knowledge layer that feeds AI agents.
• Fluency in French and English.

Culture & Benefits

• Hybrid workplace in Paris.
• Strategic scope across three European markets for Customer Care quality excellence.
• Cross-functional collaboration with Product, Legal, and the AI Lab through quality data.
• AI-focused initiative: ownership of the knowledge layer powering the bot and AI agents.
• Team structure: six embedded specialists (two per market) with room to build depth through ownership.

Hiring process

• Hiring process lasts 20 working days.
• Interviews and evaluations across the selection steps, followed by a candidate journey review.