ኢትዮጵያ
Onsite
At the Tony Blair Institute for Global Change, we work with political leaders around the world to drive change. We help governments turn bold ideas into reality so they can deliver for their people. We do it by advising on strategy, policy and delivery, unlocking the power of technology across all three. And by sharing what we learn on the ground, so everyone can benefit. We do it to build more open, inclusive and prosperous countries for people everywhere.
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The Ethiopian Statistical Service (ESS) is undertaking a strategic effort to modernize its statistical production systems through big data, artificial intelligence, advanced analytics, and improved data governance. The Big Data and AI Consultant will support the Ethiopian Statistical Service to modernize statistical production through practical AI, big data, advanced analytics, and improved data governance. This support is expected to strengthen the quality, timeliness, reliability, and policy relevance of official statistics, with particular focus on agricultural surveys, census-related systems, and national data production processes.
This role forms part of a broader technical assistance effort supported by the Tony Blair Institute (TBI) to help ESS adopt practical, scalable, and sustainable digital and analytical solutions. The consultant will work closely with ESS technical teams to improve data validation, integrate approved alternative data sources, strengthen analytical workflows, and build internal capacity for the responsible use of AI and big data in official statistics.
The objective of the assignment is to support ESS to design, pilot, and institutionalize practical AI and big data solutions that improve statistical data quality, validation, integration, analysis, and operational efficiency, while strengthening internal technical capacity and sustainable ways of working.
A. Diagnostic Assessment and Modernization Planning
Assess existing ESS data workflows, tools, systems, and institutional practices related to data collection, processing, validation, analysis, and quality assurance. Identify key bottlenecks, duplication, inefficiencies, and opportunities for modernization, and develop a prioritized roadmap for practical AI and big data interventions.
B. AI-Enabled Data Validation and Quality Assurance
Design and support the implementation of AI-enabled methods to strengthen data quality assurance, including anomaly detection, automated consistency checks, imputation approaches for missing or incomplete data, predictive validation models, and assisted classification or coding where relevant. The focus is to reduce manual validation burden, improve reliability, and strengthen the credibility of official statistical outputs.
C. Big Data Integration and Advanced Analytics
Support ESS to identify, assess, and integrate approved alternative data sources such as satellite imagery, remote sensing data, weather data, geospatial layers, and other relevant external datasets. Apply these sources to priority use cases, including agricultural statistics, yield estimation, production prediction, validation in data-scarce areas, and triangulation of survey results.
D. Data Pipeline and Workflow Modernization
Support the modernization of data pipelines and analytical workflows by introducing practical approaches for automated cleaning, validation, reproducible analysis, metadata management, version control, and documentation. Ensure that proposed solutions are technically appropriate, maintainable, and aligned with existing ESS systems.
E. Capacity Building and Embedded Mentorship
Provide hands-on training, coaching, and mentoring ESS staff using real datasets and live institutional use cases. Capacity building should cover relevant tools and methods, including Python, R, SQL, STATA, CSPro, Sen4Stat, machine learning, AI applications, big data tools, and statistical quality assurance techniques. The emphasis should be on practical application; skills transfer and enabling ESS teams to sustain the solutions after the assignment.
F. Institutional Strengthening and Sustainability
Support ESS to develop practical institutional tools, including standard operating procedures, methodological guidance, data integration protocols, and a big data methodology playbook. Contribute to strengthening data governance, data security, privacy, and coordination arrangements required for the responsible and sustainable use of AI and big data in official statistics.
Inception report and workplan outlining methodology, priority use cases, timelines, and engagement approach.
Workflow diagnostic report and modernization backlog identifying gaps, opportunities, and priority interventions.
A document outlining the recommended technical tools and software Architecture for ESS, including why each tool is suitable and how it supports scalability, DPMES integration, security, operational needs, and long-term sustainability.
A test version of an AI tool that checks whether data in important statistical processes is accurate, complete, consistent, and ready to use.
Big data integration use-case demonstrating the use of approved external data sources, such as satellite, weather, or geospatial data, alongside survey data.
Capacity building materials and training report documenting training delivered, staff reached, and applied skills developed.
Standard Operating Procedures (SOPs) and big data methodology playbook to guide continued use and institutionalization.
Final Report and 2027 Roadmap summarizing achievements, lessons learned, sustainability measures, and next-stage recommendations.
Education
Advanced degree (MSc or PhD) in Data Science, Statistics, Computer Science, or a related quantitative field, with a multidisciplinary profile combining strong grounding in statistical methods with demonstrable experience in data science, computer science, AI, and big data systems.
Minimum of 7 years of relevant hands-on experience in big data analytics, artificial intelligence, machine learning, data engineering, statistical quality assurance, or advanced analytics.
Experience working with official statistics, agricultural surveys, census systems, public-sector data systems, or national statistical offices is strongly preferred.
Technical Skills
AI and machine learning applications including anomaly detection, prediction, classification, and validation models.
Statistical data quality assurance, including consistency checks, imputation, validation, and error detection.
Programming and analytics tools such as Python, R, SQL, STATA, CSPro, Sen4Stat, and similar.
Big data tools and environments such as Hadoop, Spark, cloud platforms, or equivalent technologies.
Data engineering, reproducible workflows, metadata management, and version control.
Integration and use of geospatial, satellite, remote-sensing, weather, or other non-survey data sources.
Strong understanding of official statistics and statistical data quality principles.
Ability to translate advanced technical methods into practical institutional solutions.
Experience delivering hands-on training, coaching, and skills transfer.
Strong analytical, problem-solving, and documentation skills.
Ability to work collaboratively with government institutions and technical teams.
Effective communication and stakeholder engagement skills.
Commitment to data governance, confidentiality, privacy, and responsible use of AI.
The consultant will work through an embedded, collaborative, and use-case-driven model. The assignment should emphasize co-development with ESS teams, practical implementation using real datasets, and progressive transfer of skills and ownership.
The work will follow a phased delivery approach: diagnostic assessment, priority use-case design, pilot implementation, capacity building, institutionalization, and roadmap for scale-up.
The assignment will be guided by the following principles:
ESS ownership and long-term sustainability.
Practical, high-impact, and scalable solutions.
Responsible and ethical use of AI and big data.
Strong data governance, confidentiality, and privacy protection.
Alignment with ESS priorities, existing systems, and Ethiopia national statistical and planning priorities.
Avoid duplication by building existing tools, platforms, and institutional capacities.
Closing Date:
2026-05-31Want to know if this job is worth applying to?