Evidence Lead, AI Access Initiative (EAII Advisors)

About the role

The Evidence Lead is the primary engine for measuring whether the AI Access Initiative's programs in India work and why. It is not a traditional monitoring and evaluation role: the focus is on generating insights that change program design in real time, with data collection for government reporting as a secondary function of about 20%. The role applies AI and machine learning tools to evaluation and is part of a founding team shaping the organization's culture and operating model.

Key responsibilities

• Measure the effectiveness of new programs from early concept tests through scaled delivery, defining what "working" means for each before launch
• Identify the right data sources (administrative, program, partner data and primary collection) and make the trade-offs between rigor, cost and speed explicit
• Design and run cost-effectiveness models with legible assumptions
• Build measurement and feedback loops into program design from the start
• Act as a thought partner to the programs team, translating findings into actionable recommendations with stated confidence
• Apply AI and ML tools to evaluation and data analysis, and build the data and modeling infrastructure for a small team
• Present evidence and data externally to funders, partners, government counterparts and the research community
• Help build a strong, values-aligned culture and hire and enable a team of monitoring, learning and evaluation professionals

Required profile

• 7-9 years of experience in M&E, impact evaluation or applied research in global health or international development
• Background in a rigorous evidence environment such as academic research, a think tank or a research lab
• Experience working closely with programs and understanding how program needs differ from research needs
• Ability to navigate Indian government data systems and state-level reporting requirements
• Track record in early-stage, resource-constrained environments and initiative in taking ownership of unassigned problems

Required skills

• Applied AI and ML fluency in evaluation and data analysis
• Building and updating cost-effectiveness analyses and explaining assumptions to non-technical decision-makers
• Clear writing and presentation to funders, government counterparts and technical peers