Data Scientist & Analyst (m/w/d)

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The World Food Programme is the worlds largest humanitarian organization saving lives in emergencies and using food assistance to build a pathway to peace stability and prosperity for people recovering from conflict disasters and the impact of climate change.
At WFP people are at the heart of everything we do and the vision of the future WFP workforce is one of diverse committed skilled and high performing teams selected on merit operating in a healthy and inclusive work environment living WFPs values (Integrity Collaboration Commitment Humanity and Inclusion) and working with partners to save and change the lives of those WFP serves.
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WFP offers a highly inclusive diverse and multicultural working environment.
WFP invests in the personal & professional development of its employees through a range of training accreditation coaching mentorship and other programs as well as through internal mobility opportunities.
A career path in WFP provides an exciting opportunity to work across the various country regional and global offices around the world and with passionate colleagues who work tirelessly to ensure that effective humanitarian assistance reaches millions of people across the globe.
Data Scientist

TYPE OF CONTRACT:

Early Warning and Forecasting Unit - Analysis Performance and Planning Division
The Data Science team within the Early Warning and Forecasting Unit in the Analysis Planning and Performance (APP) division is a specialised team dedicated to addressing the challenge of modelling and forecasting food insecurity and its drivers through the innovative application of machine learning and data science techniques. By leveraging large heterogenous datasets and advanced algorithms the team can identify patterns and trends that are not immediately apparent and predict the impact of impending natural and human generated hazards enabling timely interventions and anticipatory actions in vulnerable areas. The work is crucial in facilitating data-driven decision-making for governments and humanitarian organizations allowing proactive strategies in combating food crises and their underlying causes. This proactive approach helps to mitigate the impact of potential crises on affected populations improving resilience and ensuring food security.
The Early Warning and Forecasting Unit is collaborating with WFP Nutrition and Food Quality Service on a project to generate and increase access to modelled data on risk of dietary Vitamin and Mineral inadequacy at national and sub-national levels which will be critical to inform the design and roll-out of large-scale food fortification as well as complementary programs. This project will contribute towards the improvement of core Large-Scale Food Fortification (LSFF) program performance metrics and in conjunction with data partners will achieve consensus on approach including the proxy indicator(s) for risk of dietary micronutrient risk the design of and data underlying subnational maps and use of modelling that will translate to clear and consistent programmatic guidance. Specifically the objective of the project is to develop a composite proxy-indicator for risk of inadequate dietary micronutrient intake and integrate it with in the HungerMapLIVE systems and related products. The expansion of the HungerMapLIVE to include a data layer on risk of dietary vitamin and mineral inadequacy and the potential of different programs including LSFF to fill nutrient gaps has been identified as an opportunity to support programme and policy decision-makers from national governments and other stakeholders. This activity will bring together the analytical and government technical assistance expertise of the Nutrition Knowledge and Innovation team data collection and visualization experience from the HungerMapLive Team and WFPs digital innovation and project accelerator capacities. For this project the Hunger Monitoring Unit seeks a Data
Scientist Consultant to work with the project team on the development of the methodology for accessing the risk of dietary VMD and support the integration in HungerMapLIVE.
Develop and test Machine Learning models for estimating risk of dietary micronutrient inadequacy at national and sub-national level in collaboration with the rest of the Data Science team.

• Support ad-hoc data explorations and analysis for the project by means of traditional statistical analysis as well as state-of-the-art data science methodologies.
• In collaboration with the software engineering team support the design and the
• Participate in workshops meetings and conferences to present the technical aspects of the project within the organization and externally.
• Ensure adherence to WFP policies regarding data including data privacy security management and code documentation.

~ DELIVERABLES AT THE END OF THE CONTRACT:

Technical inputs to the project methodology including the code and methodology for the validation of the proxy indicators.
Deploy machine learning models for the estimation of dietary micronutrient inadequacy in the required production environment.
Participation in project meetings scientific committee meetings and dissemination events
Presentations on the technical aspects of the project as needed.
Masters degree in engineering computer science physics mathematics or similar.

At least 3 year of professional experience in a data science-related position or field including PhD programmes and other academic positions.
Python programming skills and experience with data science libraries (pandas NumPy scikit-learn etc)
Advanced knowledge of statistics and passion for machine learning with an understanding of algorithms
Experience in designing and querying SQL databases is an asset
Knowledge of survey methodologies is an asset
Knowledge of food security and nutrition data is an asset
Experience working with the humanitarian sector or in food securitynutrition field is an asset.
Languages:
Excellent spoken and written English

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Contract