Assessment Science & ML Engineer

About This Role

Responsibilities

• Build the reproducible evidence pipeline for the two flagship assessment batteries.
• Run reliability, dimensionality, item-quality, model-fit, calibration, and DIF analyses under the approved scientific plan.
• Create data-quality gates before any item or instrument promotion.
• Maintain analysis provenance, model versions, limitations, and audit-ready outputs.
• Support outcome-data collection and the first criterion-validity studies.
• Contribute accurate production metrics to the technical manual and enterprise review packages.
• Evaluate AI scoring components and integrity flags without using emotion inference or biometric categorization.

Requirements

• Strong Python or R and SQL skills, with production-quality data analysis habits.
• Applied experience with statistical modeling, experiment design, measurement, model evaluation, or psychometrics.
• Ability to explain assumptions, uncertainty, limitations, and data-quality failures clearly.
• Experience building reproducible pipelines, versioned analyses, and reviewable technical reports.
• Professional English. French is strongly preferred.
• A relevant advanced degree or equivalent applied evidence.