Building peace in the minds of women and men
Consultancy: Learning Assessments and SDG 4 Indicator Methodology
Location
Canada
Posted
5 days ago
Salary
0
Seniority
Mid Level
No structured requirement data.
Job Description
Consultancy: Learning Assessments and SDG 4 Indicator Methodology
UNESCO
Role Description As the custodian agency for SDG 4 indicators, the UNESCO Institute for Statistics (UIS) is responsible for developing methodologies, maintaining global databases, supporting countries in reporting data, and producing technical guidance to improve the quality, comparability and use of education statistics. Learning assessments play a central role in SDG 4 monitoring, both for reporting on SDG indicator 4.1.1 and for producing several other indicators derived from cross-national assessment programmes. To support countries, assessment providers and policymakers, UIS develops technical guidance, decision-support tools and reporting mechanisms, including: - Criteria for reporting on SDG indicator 4.1.1 - Vetting mechanism - Learning Assessment Buyer's Guide UIS is also responsible for maintaining and disseminating a range of SDG 4 indicators derived from learning assessments and other data sources, together with their associated metadata and methodological documentation. As part of its ongoing work programme and preparations for regional consultations and the UNESCO Conference on Education Data and Statistics, UIS is seeking a consultant to support the revision and development of guidance materials and communication products, contribute to methodological work on learning indicators and indicators derived from cross-national assessment programmes, update selected SDG 4 indicator datasets and documentation, and prepare analytical products to strengthen the dissemination and use of UIS education data. Assignments Under the overall authority of the Head of the Foresight, Research and Methodology Section, the consultant will perform the following tasks: - Revision of the Buyer's Guide to International Student Assessments (2025 edition): Review documents and inputs, consult with partner organizations, incorporate feedback, and draft a revised Buyer's Guide. - Learning Assessments Online Decision Tree: Develop a concept and implementation proposal for an interactive online decision tree. - Vetting Mechanism communication document: Develop a concise communication document to support the positioning and promotion of the UIS Vetting Mechanism. - Discussion Paper on learning indicators: Prepare a discussion paper reviewing key methodological, conceptual and implementation challenges related to learning indicators. - Technical Note on indicators derived from cross-national assessments: Prepare a technical note reviewing methodologies used to produce SDG indicators derived from cross-national assessment programmes. - Update of SDG indicators and metadata: Update datasets for selected indicators, validate and revise datasets, and document all revisions made. Contract Duration The consultancy will be carried out over a period of 5 months, with an expected start date shortly after the completion of the selection process. The assignment will be home-based (remote), with deliverables submitted according to the schedule below. Deliverables - Revised Learning Assessment Buyer's Guide: Draft framework and recommendations by July 2026; final revised Buyer's Guide by August 2026. - Learning Assessments Online Decision Tree: Concept and implementation proposal by August 2026. - UIS Vetting Mechanism communication document: Communication document by August 2026. - Discussion Paper on learning indicators: Discussion paper by August 2026. - Technical Note on indicators derived from cross-national assessment programmes: Technical note by September 2026. - Statistical fact sheet on bullying prevalence: Fact sheet by September 2026. - Updated SDG indicator datasets and documentation: Updated datasets and documentation by October 2026. Qualifications - Advanced university degree (Master's or equivalent, or higher) in statistics, demography, economics, mathematics or related domains. Requirements - A minimum of 8 to 10 years of relevant work experience in applied social statistics, ideally related to the assessment of learning outcomes. - Preferably 3 to 5 years acquired at the international level within a UN or similar agency, or a government ministry. - Demonstrated expertise in education statistics, learning assessments and SDG 4 monitoring. - Strong analytical skills, including the ability to review indicator methodologies, validate datasets and assess comparability across data sources. - Excellent drafting and communication skills. - Solid knowledge of cross-national learning assessment programmes and of the SDG 4 monitoring framework. - Ability to work independently in a remote setting, manage multiple concurrent deliverables and meet tight deadlines. - Proven research experience in education is desirable. Languages - Excellent proficiency in English (written and spoken) is required. - Knowledge of other official UNESCO languages (French, Spanish, Arabic, Chinese, Russian) is an asset. Application Process Interested candidates should complete the on-line application, download and complete the Employment History form. At the end of the Word file, insert extra pages with the following required information: - Part 1: Technical Proposal - An up-to-date curriculum vitae; - A statement indicating how their qualifications and experience make them suitable for the assignment; - An indication of the approach (methodology, detailed workplan) he/she would adopt to carry out the assignment; - Part 2: Financial Proposal - A competitive fee for the assignment, quoted in EUR/USD/CAD/GBP and expressed on a monthly basis. Selection and Recruitment Process All candidates must complete an on-line application and provide complete and accurate information. The process may include pre-recorded video interviews and/or written assessments, interviews with a Panel, as well as reference checks. Additional Information UNESCO recalls that paramount consideration in the appointment of personnel shall be the necessity of securing the highest standards of efficiency, technical competence and integrity. UNESCO applies a zero-tolerance policy against all forms of harassment. Individuals from minority groups and indigenous groups and persons with disabilities are equally encouraged to apply. All applications will be treated with the highest level of confidentiality. UNESCO does not charge a fee at any stage of the hiring process.
Related Guides
Related Job Pages
More Machine Learning Engineer Jobs
Machine Learning Engineer
Critical SoftwareCritical Software is proud to be a Benefit Corporation, committed to making a positive impact on society, workers, the community, and the environment, in addition to profit. We are an equal opportunity workplace and committed to allowing candidates with disabilities or neurodevelopmental conditions to prove their competencies to their full potential.
Role Description We're looking for a hands-on and forward-thinking Machine Learning Engineer. In this role, you will be supported by a dedicated team, equipped to aid your success and bolster our rapid growth. At Critical, we deliver software solutions and consulting in complex, business-critical environments aimed at assisting our clients in achieving their business objectives through cutting-edge software solutions. The ideal candidate will design, build, and deploy machine learning models and pipelines that solve real business problems across various departments. You will work hands-on across the full ML lifecycle — from data preparation and model development to deployment and monitoring — collaborating closely with stakeholders to turn their needs into robust, production-ready solutions. Qualifications - Bachelor's or Master's in Computer Science, AI, Machine Learning or a related field. - 5+ years of experience in software development, with hands-on experience building and deploying machine learning models. - Solid coding skills (Python and relevant ML frameworks) and experience with the ML lifecycle from data to production. - Clear oral and written communication skills for working with teammates and stakeholders. - Practical, detail-oriented approach to debugging and improving models and pipelines. - Ability to manage your own tasks and prioritize effectively, even with some ambiguity. - A natural interest in ML techniques — old and new — and how they apply to real business problems. Requirements - Design, train, and evaluate machine learning models to address specific business problems. - Build and maintain data pipelines and infrastructure to support model development and deployment. - Deploy ML models into production and monitor their performance, reliability, and drift over time. - Identify and resolve technical issues, bugs, and blockers as they arise during development and deployment. - Work closely with team members across various departments to understand their data, processes, and needs, and adapt solutions accordingly. - Iterate on deployed models to keep them accurate, efficient, and useful as needs evolve. Benefits - Private health insurance - Employee Assistance Programme (mental health, legal, financial support) - Home office support - Extra holidays: 2 additional days after year one, more as time goes on - Extra parental leave: 2 additional months fully paid - Gradual return-to-work support: We'll help you ease back from long breaks - Sabbatical programme for long-term employees - Training, mentorship, and growth opportunities: we'll invest in where you want to go next
MLOps Engineer
Redolent, IncERM - Rina María Cabrera / Capgemini | North América External Resource Manager Tel.: +1 888 229 2961 Email: rina.cabrera@capgemini.com
Role Description - Design and implement scalable model serving platforms for both batch and real-time inference - Build model deployment pipelines with automated testing and validation - Develop monitoring, logging, and alerting systems for ML services - Create infrastructure for A/B testing and model experimentation - Implement model versioning and rollback capabilities - Design efficient scaling and load balancing strategies for ML workloads - Collaborate with data scientists to optimize model serving performance Qualifications - 7+ years of software engineering experience, with 3+ years in ML serving/infrastructure - Strong expertise in container orchestration (Kubernetes) and cloud platforms - Experience with model serving technologies (TensorFlow Serving, Triton, KServe) - Deep knowledge of distributed systems and microservices architecture - Proficiency in Java and experience with high-performance serving - Strong background in monitoring and observability tools - Experience with CI/CD pipelines and GitOps workflows
• Research, develop and deploy advanced machine learning solutions for image analysis, image processing, object detection and segmentation. • Evaluate technical challenges and determine the most effective ML approach, whether developing custom models or adapting state-of-the-art solutions. • Explore emerging technologies and techniques to continuously improve product capabilities. • Design, develop and maintain robust software solutions using modern software engineering practices. • Integrate machine learning algorithms into high-performance desktop applications. • Contribute to the wider C++ application architecture and codebase. • Participate in code reviews and contribute to development best practices. • Profile and optimise model execution for CPU and GPU acceleration. • Ensure efficient processing of large-scale 3D and 4D imaging datasets. • Develop scalable and maintainable solutions suitable for production environments. • Provide technical guidance, mentoring and support to other software engineers and test team members. • Contribute to technical investigations and architectural decisions. • Share knowledge and promote engineering excellence across the team. • Work closely with software engineers, scientists, product managers and domain experts. • Support product development from concept through to deployment. • Occasionally travel to other Oxford Instruments sites and customer locations when required.
• You will work on the backend services behind our route-planning estimator and other operational estimators (e.g., process durations, driver availability) and ensure their stability and maintainability. • You take responsibility for code quality and structure in our ML repositories (reviews, refactorings, architecture). • You work closely with our Data Scientists and reliably bring models and feature pipelines into production. • You develop and operate the associated data and training pipelines in Databricks. • You build and maintain CI/CD pipelines in Azure DevOps – including automated tests and deployments. • You ensure that our systems run reliably in the cloud environment, analyze production incidents and derive sustainable improvements for code and processes.


