Full-Time
MBZUAI is seeking a highly motivated Applied Research Scientist to lead the conceptual design and development of a robust agricultural data platform to support AI-enabled advisory, research, and deployment initiatives. The role will focus on defining data requirements, governance frameworks, incentive mechanisms, and end-to-end data pipelines that enable reliable, ethical, and scalable agricultural data collection and use, particularly in complex and resource-constrained contexts. The position will own the intellectual and architectural design of the data platform, working closely with engineering teams responsible for implementation, as well as with internal researchers and external partners to ensure the platform reflects real-world constraints and stakeholder needs.
Key Responsibilities
Data Platform Design and Architecture
– Own the end-to-end conceptual design of the agricultural data platform, including dataset prioritization, data schemas, metadata standards, and interoperability considerations.
– Identify critical categories of agricultural data (e.g., agronomic, environmental, behavioral, operational, and outcome-related) required to support AI model development, evaluation, and deployment.
– Translate research and operational objectives into coherent data architectures that can be implemented and maintained at scale.
Data Collection and Incentive Design
– Design incentive mechanisms and participation models that encourage high-quality, reliable data contribution from diverse stakeholders, including farmers, implementers, and partner organizations.
– Assess trade-offs between data quality, cost, burden, and coverage, and incorporate these considerations into data collection strategies.
– Anticipate and mitigate risks related to strategic behavior, bias, and data distortion arising from incentive structures.
Data Governance and Stewardship
– Lead the development of data governance frameworks addressing access control, data ownership, consent, reuse, and ethical considerations.
– Ensure governance approaches are aligned with institutional values, regulatory requirements, and public-interest considerations.
– Define roles and responsibilities for data stewardship across the platform lifecycle.
Data Pipelines, Quality, and Onboarding
– Specify data sanitization, validation, and quality assurance protocols to ensure reliability and usability of collected data.
– Design onboarding pipelines for new datasets and data partners, including documentation, schema validation, and provenance tracking.
– Work with engineering teams to ensure platform workflows reflect research and governance requirements.
Cross-functional and External Collaboration
– Collaborate closely with engineers, AI researchers, and program teams to align data platform design with technical and deployment needs.
– Engage with external stakeholders and partners to understand contextual constraints and incorporate field realities into system design.
– Contribute to internal knowledge-sharing through design documents, frameworks, and applied research outputs.
Academic Qualifications Required
– Master’s degree in data science, computer science, economics, information systems, social sciences, or a related field.
– PhD or equivalent applied research experience in data systems, applied machine learning, development research, or a related area is an advantage but not required.
Professional Experience Required
Essential:
– Minimum of four (4) years of experience (after graduation) in applied research, data platform design, or data-intensive system development.
– Demonstrated experience designing or contributing to end-to-end data systems, including data collection, governance, and quality management components.
– Strong ability to work across disciplinary boundaries and communicate effectively with technical and non-technical stakeholders.
Preferred:
– Experience working with agricultural, environmental, or development focused data, particularly in low- and middle-income country contexts.
– Exposure to incentive design, data governance, or responsible data practices in real-world deployments.
– Experience collaborating with engineering teams to translate conceptual designs into implemented systems.
