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Your Role

The Portfolio and Platform Lead will play a central role in managing the lifecycle of MNCNH’s AI projects, from proof-of-concept to regional or national scale across maternal, newborn, and child health programs. This individual will bridge the gap between product development and operational implementation, ensuring AI initiatives are both technically sound and scalable in the antenatal, intrapartum, postnatal, and early-childhood care settings where MNCNH operates. You will provide centralized oversight of the MNCNH AI portfolio, including the grants and investments that deliver it, ensuring projects move efficiently through development, adoption, and scale-up phases in coordination with the foundation-wide AI Steering Committee. You will coordinate with internal MNCNH technical leads, country teams, grantees, and external partners to optimize resources and maximize impact on maternal and child outcomes.

What You’ll Do

Portfolio and Product Management

• Manage AI projects and supporting grants/contracts from concept through scale.
• Build frameworks to track timelines, budgets, milestones, and grant performance.
• Assess project readiness at each stage and inform follow-on investment decisions.
• Identify and address product bottlenecks across the portfolio.
• Keep the portfolio aligned to the MNCNH Clinical Decision Support and Patient Self-Care AI Strategy.
• Work with Program Officers to source and design new grants and contracts, including investment memos, and guide proposals through the foundation’s review process.
• Serve as a technical thought partner to grantees, helping them hit milestones, course correct, and connect to the broader AI portfolio.

User Experience and Product Design

• Embed human-centered design so tools reflect the workflows, literacy levels, and constraints of frontline cadres in LMIC settings.
• Identify usability gaps (interface, accessibility, low-bandwidth performance) and drive fixes with partners and grantees.
• Commission and synthesize user research to inform portfolio priorities and partner expectations.
• Set portfolio-wide design standards covering trust indicators, error handling, explainability, and workflow integration.

Stakeholder Coordination and Strategy

• Align product strategies with organizational goals across AI teams, grant managers, grantees, and external partners.
• Build relationships with ministries of health, country MNCH programs, multilateral partners, peer funders, and implementers to support AI adoption in service delivery.
• Develop AI product introduction strategies that reflect operational realities in LMIC contexts.
• Organize and facilitate partner convenings to share learning, align on standards, and surface cross-cutting opportunities and risks.

Scaling and Resource Optimization

• Identify high-potential AI initiatives (risk-stratification algorithms, clinical decision support, and diagnostics for low-resource settings) and guide them through the maturity pipeline.
• Identify barriers to scale and connect products to the enablers needed for adoption.

Systems Integration and Interoperability

• Define and enforce integration standards so products connect with existing health information systems (DHIS2, OpenMRS, national HMIS, and MNCH registries such as antenatal care, immunization, and birth/death notification) rather than operating as point solutions.
• Oversee API design and data exchange to ensure interoperability with FHIR-compliant systems, national registries, and partner platforms.
• Identify and mitigate integration failure modes (data pipeline fragility, authentication friction, workflow disruption, and alert fatigue) that drive low uptake.
• Maintain an integration landscape map tracking which products are embedded in MNCH care-continuum workflows and drive a strategy to close the gaps.

Deliverables

• AI Project Roadmap, Timelines & Dashboards. Central tracker for prioritized AI initiatives and their grants.
• Annual Portfolio Performance Report. Progress, challenges, and recommendations across products and the grant portfolio.
• Scaling Readiness Framework. Standard approach for evaluating AI projects, confirming key enablers (regulatory pathway, compute and data access, evaluation benchmarks) are met before each stage gate.
• Stakeholder Engagement Plan. Plan for securing buy-in from MOH MNCH programs, multilateral partners, peer funders, and implementers in priority LMIC geographies.
• UX & Design Standards Playbook. Portfolio-wide design guidelines covering interaction patterns, explainability, trust indicators, and accessibility for MNCH frontline cadres in LMIC settings.
• Integration Landscape Map. Living inventory of products showing integration status, interoperability gaps, and a time-bound roadmap to embed those operating outside clinical workflows.
• Data Access Agreements with Grantees. Framework and executed agreements governing grantee data sharing for AI development, evaluation, and monitoring, covering data-use rights, privacy and consent, security, and downstream re-use by MNCNH and partners.
• Partner & Grantee AI Enablement. Structured technical assistance to build partner and grantee capability in AI methods and responsible use, so they can scope, deliver, and integrate AI into funded work.

Your Experience

• Advanced degree in biotech, pharmaceutical, or life sciences, or equivalent.
• 8+ years of experience managing AI, digital, or technology product portfolios, ideally in a commercial setting, with demonstrated experience managing grants, contracts, or comparable investment vehicles.
• Strong understanding of AI product development, evaluation, and adoption, including the product-layer factors (usability, integration, trust design) that determine whether capable models translate into real-world uptake.
• Experience in structuring and managing AI projects, tracking deliverables, and managing cross-functional teams and grantees.
• Ability to engage with governments, donors, peer funders, and implementing partners to drive AI adoption, with familiarity in engaging tech firms.
• Strong analytical skills to assess project and grant performance and resource allocation.

Other Attributes

• Familiarity with the challenges of scaling AI and digital health solutions in MNCH programs in LMIC contexts strongly preferred.
• Demonstrated experience embedding human-centered design or UX practice into digital health or AI product development, with the ability to commission and act on user research and to hold development partners and grantees accountable to design quality standards.
• Working knowledge of health system integration standards and architectures (FHIR, HL7, DHIS2, OpenMRS) and experience driving interoperability requirements with technical teams and implementation partners.