(Associate) Director, Program Innovation & Evidence, The AI Access Initiative

Evidence Action

3d•$142,000 - $162,000•Remote

About The Position

The AI Access Initiative (2AI), incubated at Evidence Action, is scaling to address the potential opportunities and risks of artificial intelligence for people in developing countries. The organization focuses on scaling AI-enabled 'big bets' to benefit hundreds of millions of people in low- and middle-income countries (LMICs) by operating at the intersection of global development actors, top AI labs, and leading researchers. 2AI aims to create open-sourced playbooks, toolkits, and insights for designing and launching impactful AI-enabled programs. Initially, the initiative is scaling two programs in AI in Agriculture and AI in Health. The organization is led by Kanika Bahl and advised by prominent figures in AI and economics. This work builds on Evidence Action's extensive experience in delivering cost-effective, evidence-based programs across Africa and Asia.

Requirements

7-10+ years' experience as a builder in international development, consulting, or an agriculture adjacent organization.
Experience designing and running randomized or quasi-experimental evaluations (RCTs, A/B tests, or similar) – especially to measure behavior change.
Track record translating evaluation results into product or program iteration, e.g. using comprehension or uptake data to redesign message content, format, or delivery channel.
A proven track record of significantly scaling organizations and/or programs to deliver impact at scale, preferably in close partnership with national governments.
Training or early career experience in a high-rigor environment (e.g., top-tier management consulting, high-growth NGO program leadership, strategy/econ research) and the ability to rapidly get up to speed in new technical areas including AI/ML forecasting concepts.
Demonstrated success leading international development programs from idea to scaled delivery, including managing multi-stakeholder coalitions with governments, research institutions, NGO implementers, and private sector partners.
A track record as a strong people manager and builder: able to set clear standards, empower others, and cultivate a high-performance culture across distributed and field-based teams.
High EQ and the ability to influence across diverse partners, from AI/forecasting researchers to frontline delivery staff to senior government officials.
Strong fluency with AI tools, and eager to experiment, learn, and adapt to emerging technologies.
Comfort with ambiguity and fast iteration; can define and test hypotheses, adapt quickly, and maintain focus on impact.
Excellent written and verbal communication skills; able to synthesize complex technical ideas (e.g., AI/ML weather forecasting language) into crisp narratives for different audiences.
Willingness and ability to travel extensively internationally (~20%) to India and other low-and-middle income countries.
Passion for the mission of ensuring that the benefits of frontier AI reach the world's poorest populations, while mitigating its downside risks.
Applicants must be legally authorized to work in the U.S. for roles based in the U.S.

Nice To Haves

The ideal candidate is an exceptional builder with the ability to drive outcomes in a fast-moving, high-stakes environment. Given the startup nature of this work, we seek contributors with high throughput, strong work ethic, and willingness to lean in and work extended hours.

Responsibilities

Own forecast message testing: Run A/B tests across format, framing, and delivery mechanism to best practices for what drives farmer comprehension and behavior change.
Scope and pilot new forecast delivery channels: Identify and test emerging distribution channels for forecasts, such as chatbots, WhatsApp, SMS, social media, etc., assessing reach, cost, and effectiveness against existing channels.
Oversee development of novel forecasts: Partner with academics to develop high-impact forecasts of new types, managing a roadmap for how new forecasts are prioritized, developed, and benchmarked. This could include hiring and managing post-doctoral students to support in model benchmarking or dataset support.
Support monitoring and evaluation (M&E): Coordinate with in-country teams to implement M&E and identify concrete ways to improve programs based on the insights.
Lead cost-effectiveness analysis: Develop and update cost-effectiveness (CE) analyses for our agriculture programs, and stay abreast of the emerging evidence base.
Scope other agriculture innovations: Assess new directions for the portfolio, including benchmarking, advisory services, and entirely new intervention types.
Partner with stakeholders: Create new and nurture existing relationships with academic institutions, research groups, delivery partners, and other agriculture organizations.
Develop best practices and thought leadership: Across all of the above, build best practices and replicable toolkits that can be applied elsewhere by others. Share key learnings with the sector through toolkits, blogs, and other public-good resources.