Technical Program Officer – Data and AI products for Humanitarian and Development Use

Responsibilities

AI product development coordination

• Define project scope, develop roadmaps, set milestones, and translate business requirements into actionable technical tasks.
• Drive AI project development lifecycle, manage dependencies, track progress, ensure quality, and oversee deployment.
• Proactively identify, analyze, and mitigate technical and operational risks; resolve roadblocks and technical issues.
• Provide regular status updates, report progress to senior management, internal and external stakeholders.
• Play a key role in identifying, onboarding, and coordinating external surge-capacity partners (private sector, academia, consultancies), ensuring that technical inputs are aligned with project needs and UNGP quality standards.
• Support the design and implement data pipelines and systems, including data ingestion, cleaning, integration, and transformation, identifying opportunities for automation and scalable engineering solutions. Coordinating with internal and external partners to ensure alignment with UNGP standards.
• Train ML models; identify and test relevant open source and partner-provided models. Implement mechanisms for model performance experiments tracking
• Deploy ML models and implement model monitoring systems to detect drift and performance degradation.
• Ensure appropriate, reproducible and aligned MLOps practices within UNFP.

Strategic Analysis, Concept Development & Partnership Support

• Support the UN Global Pulse Accelerator Programme by helping teams strengthen their piloted innovations using feasible data/digital concepts, identifying appropriate methodologies, and coordinating with technical partners and mentors.
• Conduct research and analysis on emerging data science and AI trends to inform strategic planning, portfolio development, and the design of technically robust innovations.
• Contribute analytical input to Accelerator’s portfolio selection, helping assess project feasibility, technical risk, and potential for scale within the UN system.
• Support strategic partnerships by advising on the technical feasibility of partner proposals and identifying where external expertise can complement UNGP’s capabilities.
• Help translate emerging AI/data trends into practical implications for UNGP’s portfolio, the Accelerator, and UN-wide capacity development.
• Provide input into concept notes, proposals, and partnership conversations, articulating how data/AI can be responsibly and realistically integrated into UN solutions.

Knowledge Integration & System Learning

• Bridge between technical partners and non-technical UN stakeholders, ensuring clarity in expectations, deliverables, and responsible AI requirements.
• Develop knowledge products, technical briefs, dashboards, and visualisations that communicate insights, prototype performance and model results to technical and non-technical audiences, including partners and donors, and support the integration of these learnings into ongoing and future UNGP initiatives.
• Advance knowledge integration and capacity building by developing guidance, documenting best practices and lessons learned, and tracking progress on data- and AI-driven initiatives across the UN Global Pulse network.
• Support the monitoring of partner contributions (including surge teams), ensuring learning loops and knowledge transfer back into UNGP systems.
• Contribute to community-building efforts by engaging with Finland’s and the wider UN’s innovation ecosystems (academia, startups, data-for-good actors).

Skillset

• Advanced university degree (Master’s degree or equivalent) preferably in computer science, data science, statistics, or a related field with 2 years of relevant experience or a first-level university degree preferably in computer science, data science, statistics, or a related field with 4 years of experience.
• Relevant experience is defined as work experience in one or more of the following: applied data science, applied analytics, AI product development, AI engineering, business intelligence.
• Proficiency in Python is required.
• Hands-on experience applying data science and machine learning methods to support decision-making, product development, or operational innovation is required.
• Hands-on experience in developing data visualizations or analytical outputs using modern open-source or code-based tools is required.
• Experience in partnership coordination and stakeholder management.
• Experience in the applied use of AI, digital and data expertise in humanitarian and development contexts.
• Experience with project management methodologies for software development (i.e. Agile, Scrum).