MEL Associate (Health)

Job Description

The MERLI Associate will support the Monitoring, Evaluation, Research, Learning, and Impact Creation (MERLI) team in developing MEL plans, logical frameworks, and work plans; supporting data collection through field visits; contributing to research protocol development and ensuring ethical approvals; conducting data analysis; and preparing reports and presentations for internal use and external dissemination. The role requires flexibility to support other tasks as needed.

Key Responsibilities

• Support the MEL team in developing the overall MEL plan, logical frameworks, and work plans.
• Conduct field monitoring visits and assist in gathering qualitative insights during project data collection activities.
• Support the MEL team in developing research protocols and ensure approval from the required Research Ethics Committee and local IRB (as applicable), ensuring the protection of human subjects.
• Assist in the development of reports, presentations, and templates for effective internal use and dissemination at the state and national levels.
• Maintain the highest standards of data confidentiality and privacy.
• Perform other duties as assigned by the supervisor from time to time.

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.