Applied Research Scientist

Full-Time

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.