{"id":2164957,"url":"https://alion.io/job/sagri-research-scientist-2","title":"Research scientist","company":{"id":9575,"name":"Sagri","domain":"sagri.tokyo","url":"https://alion.io/company/sagri","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HERP","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tokyo, Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":4000000,"max":9000000,"currency":"JPY","period":"year","gross":null,"usd_annual":56835},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EC2","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"Discord","optional":false},{"name":"Django REST Framework","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"Machine Learning","optional":false},{"name":"Next.js","optional":false},{"name":"PostGIS","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"React.js","optional":false},{"name":"Redux","optional":false},{"name":"Rust","optional":false},{"name":"Slack","optional":false},{"name":"TypeScript","optional":false},{"name":"Django","optional":true},{"name":"JavaScript","optional":true},{"name":"Kubernetes","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-10-09T08:36:03Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-13T13:08:45Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Research scientist\nDescription\n# Recruitment Background\nSagri 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.\nThe 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.\nWe 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.\n# Mission of the Position\nOur 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.\nAs a Research Scientist, you will develop digital technologies and models that power these applications, including:\nprediction and estimation models for soil organic carbon and greenhouse gas emissions, and their implementation in carbon credit and MRV methodologies,\nmonitoring approaches for tillage, cover crops, crop growth, and related management practices using satellite data, geospatial information, and machine learning,\nyield 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.\nThrough 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.\nWe 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.\n# Job Description\n◆Specific Job Responsibilities\nCollaborate 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.\nIntegrate satellite, geospatial, and field-observation datasets, and organize, clean, and preprocess data for machine learning and process-based model calibration.\nDevelop 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.\nDevelop 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.\nContribute 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.\n◆Sagri's Development Environment\nSagri 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.\n＜Technologies＞\nLanguages: TypeScript / Python / Rust\nArchitecture: Redux / Atomic Design\nLibraries/Frameworks: React.js / Next.js / Django REST framework\nDB: PostgreSQL (PostGIS)\nInfrastructure: AWS (EC2 / EKS / S3 / RDS, etc.)\nVersion Control: Git Repository\nManagement: GitHub\nCommunication Tools: Slack / Zoom / Discord / Google meet\n# Culture\n◆About the Department\nDepartment: Engineering Dept.\nMembers: 11 [TT1.1]full-time employees\nSupervisor: CRO Tanaka\n◆Department Culture\nSagri 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.\n#Selection Process\n- Document screening\n- First Interview + Presentation\n- Second interview\n- Final interview\n- Offer meeting\nJoin us!\nRequirements\n# Recruitment Background\nSagri 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.\nThe 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.\nWe 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.\n# Mission of the Position\nOur 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.\nAs a Research Scientist, you will develop digital technologies and models that power these applications, including:\nprediction and estimation models for soil organic carbon and greenhouse gas emissions, and their implementation in carbon credit and MRV methodologies,\nmonitoring approaches for tillage, cover crops, crop growth, and related management practices using satellite data, geospatial information, and machine learning,\nyield 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.\nThrough 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.\nWe 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.\n# Job Description\n◆Specific Job Responsibilities\nCollaborate 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.\nIntegrate satellite, geospatial, and field-observation datasets, and organize, clean, and preprocess data for machine learning and process-based model calibration.\nDevelop 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.\nDevelop 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.\nContribute 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.\n◆Sagri's Development Environment\nSagri 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.\n＜Technologies＞\nLanguages: TypeScript / Python / Rust\nArchitecture: Redux / Atomic Design\nLibraries/Frameworks: React.js / Next.js / Django REST framework\nDB: PostgreSQL (PostGIS)\nInfrastructure: AWS (EC2 / EKS / S3 / RDS, etc.)\nVersion Control: Git Repository\nManagement: GitHub\nCommunication Tools: Slack / Zoom / Discord / Google meet\n# Culture\n◆About the Department\nDepartment: Engineering Dept.\nMembers: 11 [TT1.1]full-time employees\nSupervisor: CRO Tanaka\n◆Department Culture\nSagri 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 Sag...","description_format":"text","description_chars":14846,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":true},"security_clearance":false,"languages":[{"language":"Japanese","level":"Advanced (C1)","optional":false}]},"benefits":["Health insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Geospatial AI","Agriculture AI"],"lifecycle":[{"event":"open","at":"2026-10-09T09:40:29Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":3,"expected_fill_days":20,"reasons":["conf:0","win:early"],"computed_at":"2026-10-13T05:45:00Z"},"pay":{"stated_usd_annual":56835,"is_top_pay":false},"html_url":"https://alion.io/job/sagri-research-scientist-2","json_url":"https://alion.io/job/sagri-research-scientist-2.json","meta":{"generated_at":"2026-10-13T13:41:10Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":21054,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-14T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":9575},"rest":"https://alion.io/mcp/rest/get_company?id=9575"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsagri-research-scientist-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fsagri-research-scientist-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fsagri-research-scientist-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/sagri-research-scientist-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsagri-research-scientist-2"}]}