# Recruitment Background Sagri is developing digital agricultural technologies that combine digital soil mapping, satellite data, machine learning, and process-based crop models to support both agricultural productivity and environmental value creation. Our work spans two complementary areas: carbon credit and MRV services that quantify soil carbon sequestration and greenhouse gas emissions, and crop production analytics that use models such as APSIM and DSSAT together with machine learning for yield prediction, fertilizer and irrigation optimization, and assessment of yield losses caused by drought, flooding, and other environmental stresses. The Sagri Applied Science team integrates satellite observations, weather, soil, crop management, and field data to develop scientifically robust and scalable solutions for farmers and supply-chain stakeholders. For carbon markets, this includes methodologies that can demonstrate changes in soil organic carbon and greenhouse gas emissions in a scientifically defensible and verifiable manner. For crop production, it includes predictive and decision-support approaches that improve input-use efficiency, yield stability, and resilience to climate risks. We are seeking Research Scientists who are excited to advance both of these areas and translate agronomic science, data science, and process-based modeling into practical digital solutions. # Mission of the Position Our applications are designed to collect only the minimum necessary input from farmers and supply-chain stakeholders while integrating satellite, weather, soil, and crop management data to evaluate and predict both environmental outcomes and crop productivity. In addition to estimating soil organic carbon stocks and greenhouse gas emissions, we aim to quantify crop yield, responses to fertilizer and irrigation, and yield risks associated with drought, flooding, and other stresses. As a Research Scientist, you will develop digital technologies and models that power these applications, including: • prediction and estimation models for soil organic carbon and greenhouse gas emissions, and their implementation in carbon credit and MRV methodologies, • monitoring approaches for tillage, cover crops, crop growth, and related management practices using satellite data, geospatial information, and machine learning, • yield prediction, fertilizer and irrigation optimization, and drought/flood stress impact models that combine process-based crop models such as APSIM and DSSAT with machine learning. Through these technologies, we aim to contribute both to climate change mitigation through carbon credit generation and MRV, and to agricultural productivity and climate resilience through yield stabilization, input optimization, and climate-risk assessment. We welcome candidates with expertise in geospatial data science, machine learning, statistical or mathematical modeling, together with a solid foundation in agricultural science, particularly agronomy, soil science, or crop physiology. Experience using, calibrating, and validating process-based crop models such as APSIM or DSSAT is especially valued. A PhD in agricultural science or a closely related field is strongly preferred. # Job Description ◆Specific Job Responsibilities • Collaborate with field teams in Japan and internationally to collect and organize data on crop growth and yield, soils, weather, fertilizer and irrigation management, and greenhouse gas emissions. • Integrate satellite, geospatial, and field-observation datasets, and organize, clean, and preprocess data for machine learning and process-based model calibration. • Develop and validate models related to soil organic carbon, greenhouse gas emissions, tillage, and cover crops, and support the scientific development of carbon credit and MRV methodologies. • Develop and validate methods for crop yield prediction, fertilizer and irrigation optimization, and assessment of yield losses caused by drought, flooding, and other environmental stresses using crop models such as APSIM and DSSAT together with machine learning. • Contribute to proof-of-concept projects from research hypothesis development through data collection, analysis, model development, scientific writing, intellectual property creation, and consideration of implementation in products, with guidance from senior researchers or mentors. ◆Sagri's Development Environment Sagri already has multiple products with strong growth potential, and our research and development themes are broad. This role offers the opportunity to work on scientifically challenging and socially impactful projects spanning carbon and MRV, crop yield prediction, input optimization, and climate-risk assessment at the intersection of agriculture, climate technology, and digital innovation. You do not need to be an expert in every area from day one; we welcome candidates who are motivated to learn, take on new challenges, and expand their expertise. ＜Technologies＞ Languages: TypeScript / Python / Rust Architecture: Redux / Atomic Design Libraries/Frameworks: React.js / Next.js / Django REST framework DB: PostgreSQL (PostGIS) Infrastructure: AWS (EC2 / EKS / S3 / RDS, etc.) Version Control: Git Repository Management: GitHub Communication Tools: Slack / Zoom / Discord / Google meet # Culture ◆About the Department Department: Engineering Dept. Members: 11 [TT1.1]full-time employees Supervisor: CRO Tanaka ◆Department Culture Sagri is a private company, but we offer an environment where you can engage in research activities alongside your duties. You can continue your career in writing papers, attending conferences, and participating in academic societies. Depending on your achievements and suitability, we can consider arrangements where you remain affiliated with a university or research institution while working at Sagri (such as contracting or joint research). We welcome individuals who wish to build their careers in academia or research institutions in the future. #Selection Process - Document screening - First Interview + Presentation - Second interview - Final interview - Offer meeting Join us!

◆Required Experience/Skills ・Proven research achievements using mathematical modeling, statistical analysis, machine learning, or geospatial data analysis. ・Experience in data analysis and development of machine learning or statistical models using Python or similar tools. ・Experience with fieldwork in agriculture or environmental science, or model development using field-observation data. ・Ability to independently write peer-reviewed scientific papers (PhD-level research skills). ・Proficiency in presenting and discussing research in English, including communication with international teams and reviewing or writing academic papers.

◆Preferred Experience/Skills ・A PhD in agronomy, crop science, soil science, agricultural engineering, environmental science, data science, or a related field. ・Experience using, calibrating, or validating process-based crop models such as APSIM or DSSAT. ・Research experience in crop yield prediction, fertilizer optimization, irrigation optimization, or climate-risk assessment including drought and flooding. ・Experience with remote sensing, GIS, geospatial data, or machine learning/statistical analysis of time-series data. ・Research or practical experience related to soil organic carbon, greenhouse gas emissions, carbon credits, or MRV.

◆ Who We Want to Work With ・Shares Sagri’s vision, mission, and values. ・Proactively collaborates with universities and research institutions. ・Challenges themselves to develop future-oriented products that solve social issues. ・Expands their work scope beyond their specialty and continuously catches up with new technologies. ・Values team communication and information sharing, with a high willingness to learn new skills. ・Listens sincerely to customer feedback and responds proactively. ・Enjoys working in a fast-paced environment and embraces significant changes.