Background:

UN Women, grounded in the vision of equality enshrined in the Charter of the United Nations, works for the elimination of discrimination against women and girls; the empowerment of women; and the achievement of equality between women and men. Placing women's rights at the center of all its efforts, UN Women leads and coordinates United Nations system efforts to ensure that commitments on gender equality and gender mainstreaming translate into action throughout the world. It provides strong and coherent leadership in support of Member States' priorities and efforts, building effective partnerships with civil society and other relevant actors.

To support the production and use of gender statistics across Asia and the Pacific, UN Women is implementing its flagship programme Making Every Woman and Girl Count (Women Count) across the region. Women Count aims to create a radical shift in how gender statistics are used, produced and promoted to inform policy and advocacy on gender equality. In Asia and the Pacific, the programme prioritizes several key areas of work, including the creation and use of environment statistics, women’s economic empowerment statistics, technology facilitated violence against women statistics, and statistics calculated from big data and non-conventional data sources.

As part of building an enabling environment for gender statistics, UN Women trains the producers and the users of gender data across national statistical systems (NSSs), equipping them with the skills to produce and use it. The Forging Pathways to Gender Equality in Statistical Leadership course carries this capacity building into statistical leadership: it sets out to identify and grow women leaders within national statistical offices (NSOs) and the wider NSS, and to open pathways for them to reach senior positions. Its participants are high-level officials and the decision-makers and policymakers who shape the NSS.

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly reshaping not only how gender data is generated, analyzed, communicated, and applied, but also how statistical organizations set priorities, organize work, develop their workforce, manage technology, and exercise accountability. Its adoption is therefore an institutional reform and leadership agenda—not simply a technical modernization exercise. When used well, they can help fill persistent gender data gaps and surface insights at greater scale and speed. Used without adequate leadership and safeguards, they can reproduce gender bias, deepen digital and workplace inequalities, and introduce new risks related to privacy, transparency, accountability, and public trust. Senior statistical leaders must therefore be able to assess institutional readiness, make informed adoption decisions, establish appropriate governance and oversight, and ensure that AI-enabled transformation advances gender equality both in statistical outputs and within the institutions itself.

To address this leadership need, UN Women is seeking a consultant to develop a dedicated, one-hour training module on AI, institutional readiness, and gender-responsive statistical leadership. The module is intended for delivery within a statistical leadership training at the sides of Asia-Pacific Statistics Week 2026, which will be held in Bangkok from 30 November to 4 December 2026. The materials will also be added to the online repository of training materials for future use in further implementation of The Forging Pathways to Gender Equality in Statistical Leadership course.

In connection to the above, the consultant (who will report to the Regional Gender Statistics Specialist) is expected to deliver the following outputs:

Description of Responsibilities/ Scope of Work

Ultimate result of service

The consultant is expected to develop a self-contained, one-hour training module that equips senior statistical leaders to understand the institutional implications of AI, assess their organizations’ readiness, and identify the leadership decisions and reforms required for responsible and gender-responsive adoption. The session should use plain language and an executive-learning approach, without a technical deep-dive. The module must build on and align with the structure of the surrounding leadership training - particularly its emphasis on institutional assessment, gender mainstreaming, women’s leadership and action planning, and must include a comparable suite of materials (e.g. power point presentation, syllabus, exercises, references).

The consultant will be responsible for the following:

  • Finalize the module’s learning objectives and run-of-show in consultation with UN Women, aligned with the objectives and sequencing of the surrounding leadership-training modules.
  • Develop a concise, plain-language executive orientation to AI, including ML and generative AI, and illustrate how these technologies are already entering the work of NSOs and the NSS. This orientation should establish only the shared vocabulary needed for leadership discussion and should not become technical instruction on models, coding, or tools.
  • Frame AI adoption as a gender-responsive institutional transformation agenda. The module should help leaders assess institutional readiness—including strategy, governance, infrastructure, workforce, resources, risks and accountability—while examining gender bias in AI systems and the implications for women’s jobs, skills and leadership pathways. It should identify actions to prevent AI from reinforcing inequalities and expand opportunities for women’s leadership.
  • Align the module with current regional and global efforts, particularly the AI-related agenda of the ESCAP Committee on Statistics and the work of the Kigali City Group on AI Readiness for Official Statistics. It should translate these emerging frameworks, together with the UN Fundamental Principles of Official Statistics, into practical leadership with institutional decisions.
  • Develop an interactive, gender-responsive AI-readiness self-assessment and a concise action-planning tool that enables participants to identify their institution’s current level of readiness, priority gaps, immediate leadership actions, responsible actors and opportunities for integration into existing institutional or national statistical strategies.
  • Curate real-world cases, regionally relevant cases, including from NSOs and the NSS, that illustrate leadership choices, institutional enablers, trade-offs and lessons—covering both beneficial applications and instances where AI risked undermining gender equality, inclusion, rights or trust.

  • Produce facilitator-ready materials: a slide deck, a timed facilitator guide, an interactive exercise, a gender-responsive AI-readiness and action-planning tool, a one-page leadership checklist, and an annotated resource list. Materials should be suitable for both remote and in-person delivery.

No. Key tasks Deliverables Expected completion time (due day) 
1 Finalize the training approach and workplan in consultation with relevant UN Women teams

Inception note: confirmed learning objectives, session run-of-show, content approach and workplan

30 September 2026
2 Develop draft module content and materials. Incorporate written feedback from relevant UN Women teams Draft package: slide deck, syllabus, timed facilitator guide, participatory exercise, gender-responsive AI-readiness and action-planning tool, one-page leadership checklist and annotated resource list.

15 October 2026
3 Finalize the module, fully incorporating UN Women feedback. Final package: revised deck, syllabus, timed facilitator guide, participatory exercise, finalized AI-readiness and action-planning tool, one-page leadership checklist, and annotated resource list, ready for delivery.

15 November 2026
4 Deliver the training module remotely (TBC), de-brief with UN Women after the session to discuss lessons learnt and, if required, agree on any incremental changes needed to the material.
  • Training module delivered (TBC), including participatory exercise(s).
  • Handover of improved materials (if changes were requested) and lessons to UN Women.
 End November  - Mid December 2026

Consultant’s Workplace and Official Travel

This is a home-based consultancy. The consultant is not expected to travel.

Competencies :

Core Values:

  • Integrity;
  • Professionalism;
  • Respect for Diversity.

Core Competencies:

  • Awareness and Sensitivity Regarding Gender Issues;
  • Accountability;
  • Creative Problem Solving;
  • Effective Communication;
  • Inclusive Collaboration;
  • Stakeholder Engagement;
  • Leading by Example.

Please visit this link for more information on UN Women’s Values and Competencies Framework: 

Functional Competencies:

  • Capacity building skills on gender statistics or related knowledge and skills to international audiences
  • Knowledge of inclusivity issues related to AI
  • Strong research and analytical skills especially applied to gender statistics

Required Qualifications

Education and Certification:

  • Advanced degree(s) in International Development, Social Sciences, International Relations, Gender Studies, Statistics, Mathematics, Econometrics, Computer Science or related fields;
  •  A first-level university degree in combination with two additional years of qualifying experience may be accepted in lieu of the advanced university degree.

Experience:

  • Minimum of 7 years of professional experience in the area of gender or inclusive statistics, including facilitating capacity building activities and/or developing  related learning materials, resources, or research is required
  • Experience in teaching, building capacity and/or communicating gender or inclusive statistical content tailoring to different audiences, including international and government audiences, is required 
  • Demonstrated experience designing and conducting research on gender issues, combined with a working understanding of AI, machine learning, and LLMs sufficient to help leaders grasp the technical dimensions relevant to their institutional decision-making is highly desired
  • Experience with the UN System is a strong advantage
  • Experience leading teams of researchers is required

Languages:

  • Excellent communication and writing skills in English are required

How to Apply

  • A cover letter (maximum length: 1 page)
  • Evidence (in the form of links to prior materials) of previous work of relevance for this assignment, in particular, evidence of previous work related to gender and AI or data, and to professional or executive training and capacity building, may be requested from shortlisted candidates for further assessment.

Statements :

In July 2010, the United Nations General Assembly created UN Women, the United Nations Entity for Gender Equality and the Empowerment of Women. The creation of UN Women came about as part of the UN reform agenda, bringing together resources and mandates for greater impact. It merges and builds on the important work of four previously distinct parts of the UN system (DAW, OSAGI, INSTRAW and UNIFEM), which focused exclusively on gender equality and women's empowerment.

Diversity and inclusion:

At UN Women, we are committed to creating a diverse and inclusive environment of mutual respect. UN Women recruits, employs, trains, compensates, and promotes regardless of race, religion, color, sex, gender identity, sexual orientation, age, ability, national origin, or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, competence, integrity and organizational need.

If you need any reasonable accommodation to support your participation in the recruitment and selection process, please include this information in your application.

UN Women has a zero-tolerance policy on conduct that is incompatible with the aims and objectives of the United Nations and UN Women, including sexual exploitation and abuse, sexual harassment, abuse of authority and discrimination. All selected candidates will be expected to adhere to UN Women’s policies and procedures and the standards of conduct expected of UN Women personnel and will therefore undergo rigorous reference and background checks. (Background checks will include the verification of academic credential(s) and employment history. Selected candidates may be required to provide additional information to conduct a background check.)

Note: Applicants must ensure that all sections of the application form, including the sections on education and employment history, are completed. If all sections are not completed the application may be disqualified from the recruitment and selection process.


 

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