MERLI Fellow

Job Description

The MERLI Fellow will support the Monitoring, Evaluation, Research, Learning, and Impact (MERLI) team in the development and implementation of Monitoring, Evaluation, and Learning (MEL) frameworks across a variety of projects. The Fellow will contribute to research design, data collection, quality assurance, and dissemination activities to strengthen evidence-based decision-making and program effectiveness.

• Support the design and implementation of MEL frameworks, work plans, and research protocols.
• Conduct field visits and spot checks to monitor project implementation and ensure adherence to MEL standards.
• Assist in ensuring data quality through routine checks and validation processes.
• Support research ethics approval processes and ensure compliance with ethical guidelines.
• Contribute to the preparation of reports, presentations, and knowledge products for internal and external stakeholders.
• Assist in the development and dissemination of learning materials, case studies, and success stories.
• Maintain confidentiality and uphold data privacy standards across all MEL activities.
• Provide general support to the MERLI team and perform other tasks as assigned by the supervisor.

Requirements

Requirements

M.Tech. / M.E. / M.S. / M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field with work experience of 6+ years or a PhD from a reputed institute. Experience and expertise in ASR are preferred. Experience and expertise with LLMs are nice-to-have. We are looking for ML Scientists with experience applying AI, machine learning, and data science to real-world problems. Ideal candidates should have a strong research background, have experience in mentoring junior researchers through strong code practices and ideation, and be adept at a variety of data mining / analysis methods and tools, building and implementing models, visualising data, creating / using algorithms, and running simulations. Familiarity with popular deep learning algorithms is a strong plus; however, demonstrated willingness and ability to learn will also be considered. Candidates should be comfortable working with cross-functional teams and must have excellent oral and written communication skills and a track record of driving projects to completion. Evidence of written skills through high-quality research publications is a plus. Candidates should be passionate about using their technical skills to solve large, societally important problems.