UNICEF works in some of the world’s toughest places, to reach the world’s most disadvantaged children. To save their lives. To defend their rights. To help them fulfill their potential.
Across 190 countries and territories, we work for every child, everywhere, every day, to build a better world for everyone.
And we never give up.
For every child, HOPE
Consultancy Title: Machine Learning Consultant
Section/Division/Duty Station: Division of Analysis Planning and Monitoring NYHQ
Duration: 1 November 2024 to 15 October 2025
Home/ Office Based: NYHQ / Remote
About UNICEF – DO NOT EDIT
If you are a committed, creative professional and are passionate about making a lasting difference for children, the world's leading children's rights organization would like to hear from you. For 70 years, UNICEF has been working on the ground in 190 countries and territories to promote children's survival, protection and development. The world's largest provider of vaccines for developing countries, UNICEF supports child health and nutrition, good water and sanitation, quality basic education for all boys and girls, and the protection of children from violence, exploitation, and AIDS. UNICEF is funded entirely by the voluntary contributions of individuals, businesses, foundations and governments. UNICEF has over 12,000 staff in more than 145 countries.
BACKGROUND – PLEASE REFER TO THE ATTACHMENT FOR FULL DETAILS AND INFOMATION
Purpose of Activity/ Assignment:
UNICEF requires the services of an external consultant to design, develop, test and deploy machine learning models to enhance the efficiency and effectiveness of vaccine stock management systems, subnational distribution strategies, planning processes, track adherence to stock management protocols, and strengthening vaccine accountability standard operating procedures (SOPs) through the active use of data for planning and decision-making.
The consultant will support UNICEF's Program Group Immunization Division (PG-I) in optimizing vaccine supply chains, ensuring that life-saving vaccines reach every child, regardless of geographical or logistical challenges.
The consultant will liaise with the Data, Analytics, Planning and Monitoring Division (DAPM) and with Program Group Immunization (PG-I) to deploy robust machine-learning models within the UNICEF Azure cloud environment. These models will analyze complex data sets, process natural language protocols, optimize subnational distribution routes and schedules, and predict stock risky situations across supply chains, thereby minimizing waste and ensuring timely delivery. By integrating innovative data-driven solutions into country office’s supply chain operations, this assignment aims to strengthen the organization's ability to deliver vaccines more efficiently and equitably, ultimately contributing to improved immunization coverage and the health of children worldwide.
Scope of Work: PLEASE REFER TO THE ATTACHMENT FOR FULL DETAILS AND INFOMATION
Plan on how to use the existing data sources together
Data sources available include vaccine and devices stocks (opening and closing balances), vaccine movement (new arrivals, distribution, wastage), forecasted demand (including at subnational levels), allocations, consumption (actual, forecasted, mean and target population), min/safety and max quantities of vaccines by supply chain level and by cold store, pipelines, causes and drivers of stockouts, country mitigation measures, outcomes of country engagement activities, coverage rates, number of zero-dose children, frequency and duration of stockouts, RTM Maturity model data, Cold Chain Inventory Data, EVM & Improvement Plans, Carbon Footprint data for the immunization programme, Waste Management Assessment Data and health Facility solarization Data. There is a need for an initial plan on how we use all the data sources together to strengthen supply chain planning and prediction models.
Develop, test, pilot and deploy robust machine learning (ML) models
Collect, clean, assess, preprocess SC data, and train ML model(s)
Terms of Reference / Key Deliverables:
Work Assignments Overview Deliverables/Outputs Delivery deadline
Coordinate with stakeholders (PG-I, DAPM) to review available datasets, create a plan on how to use all the available data sources together, what kind of ML models should be developed and why
• Development plan with the strategy to use the data sources, the ML models to be develop, and the ML techniques that will be used – By November 15 2024
Version 1. Develop, test and deploy ML models according to the development plan.
Data gaps, risks, limitations, assumptions, and potential solutions identified (Based on the findings of desk review, highlight data gaps, major risks, limitations and assumptions and propose how these would be resolved to ensure the ML models are robust, fit-for-purpose and highly accurate).
Suitability of Synthetic Data (SD) use assessed, sources /tools identified, and cost
Location: New York, NY
Posted: Oct. 17, 2024, 9:35 p.m.
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