Generate policy-relevant analytical outputs on learning quality and equity using AMPL datasets, working together with country teams as co-authors.
Strengthen national capacity to independently apply advanced statistical methods to learning assessment data in selected countries.
Implement a six-month phased programme combining structured training, hands-on application and technical mentoring, centred on two in-person workshops.
Assess the availability and readiness of AMPL datasets and develop a common analytical framework addressing key policy questions on learning outcomes, equity and school/system-level effects.
Prepare training materials covering advanced statistical methods supported by practical tools such as annotated code and replicable templates.

A PhD or equivalent advanced degree in statistics, econometrics, psychometrics, or a related field; OR a Master's degree in a related field with a minimum of seven (7) years of relevant professional experience.
Demonstrated expertise in analysing large-scale assessment data, including familiarity with AMPL, MILO, or equivalent international learning assessment programmes.
Strong experience in multilevel modelling and complex survey data analysis.
Proven experience in capacity building and training delivery, demonstrated by at least five (5) years of experience or three (3) comparable assignments, with references available upon request.
Proficiency in at least one statistical software package (e.g. R, Stata) for large-scale assessment data analysis.
Ability to design and deliver effective training and mentoring for national technical teams.
Excellent drafting skills, with proven ability to produce clear analytical reports, policy briefs and technical guidance for diverse audiences.
Ability to work independently in a remote setting, manage concurrent country engagements and meet deadlines.
Excellent proficiency in English (written and spoken) is required. Working knowledge of French is an asset.

Consultant, Advanced Analysis of Learning Assessment Data

Key skills