Job Description Background Information - Job-specific
The United Nations Office for the Coordination of Humanitarian Affairs (OCHA) has a Centre for Humanitarian Data in The Hague, the Netherlands which is focused on increasing the use and impact of data in the humanitarian sector. The vision is to create a future where all people involved in a humanitarian emergency have access to the data they need, when and how they need it, to make responsible and informed decisions.
The Centre has four work streams: data services, data policy, data literacy, and network engagement. The Centre’s data services work includes direct management of the Humanitarian Data Exchange (HDX) platform and the Humanitarian Exchange Language (HXL) data standard. For data policy, the Centre is developing guidelines for responsible data use; for data literacy, we offer on in-person and remote training programmes as well as a Data Fellows Programmes. Finally, the Centre engages an active community in support of its mission and objectives through a number events and communication activities.
One area of emerging interest in the humanitarian sector is predictive analytics: asking “what will happen” in a particular humanitarian context and using machine learning and the application of statistical modeling to arrive at an answer. When Mark Lowcock, the Head of OCHA, opened the Centre for Humanitarian Data in December 2017, he asked whether predictive analytics could be used to work out the next problem before it crystalizes so that humanitarians could act faster, cheaper and better to address it.
The Centre initiated its work in Predictive Analytics through the 2018 Data Fellows Programme, and has continued to invest in this area through sustained research into the development of models related to different aspects of humanitarian response. This includes ongoing projects to predicting funding allocations in South Sudan, and predicting needs related to the possible onset of El Niño in southern Africa.
There are a number of initiatives by partners to predict humanitarian need. As more organizations start investing in predictive analytics, there is an opportunity to promote improved coordination, collaboration, peer review, and collective investment in models as a public good.
The Predictive Analytics Team Lead will work to strengthen the Centre’s initial projects and develop a detailed plan for the Centre’s approach to predictive analytics.
The Predictive Analytics Team Lead will report to the Lead for the Centre for Humanitarian Data, who is based in The Hague. He or she will work closely with the Centre’s Data Partnerships Team, and the Coordinator for the Data Fellows Programme.
- Oversee and provide strategic direction to the Centre’s predictive analytics workstream.
- Oversee and conduct research related to existing pilot models, in close collaboration with OCHA divisions, branches and country offices.
- Liaise with partners in the UN, NGOs, academia and private sector on the sharing of data and model insights as well as peer review of model results.
- Design a quality assurance process for developing and releasing predictive models including the use of code and data repositories, transparent documentation, and mechanisms for peer review.
- Develop a framework for hosting OCHA and partner models and their underlying data as a public good.
- Liaise with focal points from OCHA analysis-strengthening and anticipatory financing initiatives and ensure a coordinated approach to analytical efforts.
- Lead webinars and workshops for internal and external audiences on the potential of predictive analytics for OCHA.
- Draft communications material explaining the Centre’s work on predictive analytics.
- Coordinate predictive analytics team meetings and take part in Centre team meetings and work processes.
- Utilize available project management and office communication tools to effectively track and deliver expected outputs.
- Represent the Centre at partner meetings and public events.
- A medium-term plan including objectives and key results for the Centre’s predictive analytics workstream.
- A functioning quality assurance process for developing and releasing predictive models.
- A framework for hosting models and their underlying data as a public good.
- Communications material about the Centre’s predictive work.
- Advanced university degree (Master’s degree or equivalent) in mathematics, physics, engineering, computer science, statistics or a related field. Bachelor’s degree with 9 years of experience or Master’s degree with 7 years of experience is required.
- Experience with data manipulation and transformation as well as code refactoring and API development are essential.
- Comfort within an Agile environment is expected.
- Skills in data visualization is desirable.
- Fluency in English is required (both oral and written).
- Knowledge of another UN official language is an advantage.
About the Organization
UNOPS is an operational arm of the United Nations, supporting the successful implementation of its partners’ peacebuilding, humanitarian and development projects around the world. Our mission is to help people build better lives and countries achieve sustainable development.
UNOPS areas of expertise cover infrastructure, procurement, project management, financial management and human resources.
Working with us
UNOPS offers short- and long-term work opportunities in diverse and challenging environments across the globe. We are looking for creative, results-focused professionals with skills in a range of disciplines.
With over 4,000 UNOPS personnel and approximately 7,000 personnel recruited on behalf of UNOPS partners spread across 80 countries, our workforce represents a wide range of nationalities and cultures. We promote a balanced, diverse workforce — a strength that helps us better understand and address our partners’ needs, and continually strive to improve our gender balance through initiatives and policies that encourage recruitment of qualified female candidates.
Work life harmonization
UNOPS values its people and recognizes the importance of balancing professional and personal demands.