POSITION TITLE: Assistant Technical Officer-Machine Learning
REPORTS TO: Technical Advisor-Machine Learning
LOCATION: Cross River State (Calabar, Ikom & Ogoja LGAs)
PROJECT SUMMARY
Family Health International (FHI 360) with funding from USAID/Development Innovation Ventures (DIV) is implementing the Machine Learning Project to evaluate the impact of machine learning and person-centered interventions in reducing treatment interruption (IIT) and improving overall health outcomes among PLHIV newly initiated on ART in Akwa Ibom and Cross River states.
Antiretroviral therapy (ART) for HIV treatment has transformed HIV from a fatal illness to a lifelong chronic disease. While long term adherence to ART leads to decreased morbidity and mortality and reduces the risk of viral transmission, these benefits can only be achieved if patients remain on treatment. Available data shows that Persons Living with HIV(PLHIV) face many barriers to accessing and remaining in care and treatment which has led to a substantial proportion of patients either totally interrupting or cycle in and out of treatment.
The ML project therefore aims to bridge this evidence gap by generating evidence on the impact of ML-informed person-centered care intervention on reducing interruption in treatment (IIT), integrate ML into existing data management system (LAMISPlus), and provide intervention to clients identified at high risk of interrupting treatment.
Duties and responsibilities:
Knowledge, Skills & Attributes:
Qualifications and Requirements:
Typical Physical Demand
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