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PCI India is seeking a Project Associate - WEE to lead LokOS program delivery across Bihar districts, coordinating with the state Community Finance team and district teams to ensure consistent implementation and data quality. The role emphasizes capacity building, field training, and systematic reporting.
The position is based in Patna with travel across districts, requiring a post-graduate qualification in management or related fields and at least 3 years of program coordination experience.
The Project Associate - WEE, will own the delivery and performance of the LokOS program activities across assigned districts in Bihar. S/he will be the primary point of coordination with the Community Finance (CF) team at the state level, to align program activities with state CF priorities and plans, and will coordinate with and support District Coordinators to ensure consistent implementation of the program approach. S/he will be responsible for ensuring capacity building support is extended in a meaningful manner at the district level. S/he will be responsible for conducting ToTs for the district as well as block teams of JEEViKA.
PCI intends to work on supporting JEEViKA in LokOS Data Digitization for Enhanced Credit, through a 12-month intensive engagement covering 4 districts of Bihar - Patna, Bhojpur, Muzaffarpur and Nalanda - across 70 Cluster Level Federations (CLFs) and 5.95 lakh SHG women.
The assignment requires a field-led implementation approach to translate LokOS digitisation into sustained, accurate and credit-relevant transaction data at scale. Working closely with Jeevika (Bihar State Rural Livelihoods Promotion Society), PCI will drive SHG and VO transaction-data training and adoption, data-quality assurance, handholding, operational problem-solving and structured reporting, contributing to the plan of supporting 0.8 million active, regularised transaction-data records.
The project centres on moving Bihar s SHG/VO ecosystem beyond one-time training towards sustained usage, regularised transaction data, strengthened Bookkeeper (BK) capability, and clean, integration-ready CLF-level data to support future credit-model integration.
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