We are looking for an experienced SME – Agri / Crop Science & Crop Modelling to provide technical leadership for scalable crop intelligence, monitoring, yield forecasting, stress/risk assessment, advisories, and decision-support solutions by integrating crop, weather, soil, field, satellite, and geospatial data.

Key Responsibilities:

• Lead the development, application, calibration, and validation of crop models such as DSSAT, APSIM, AquaCrop, WOFOST, or equivalent for crop monitoring, yield estimation, forecasting, and advisories.
• Develop methodologies for crop stress/risk assessment, drought and climate-impact analysis, productivity estimation, and loss assessment.
• Integrate field/CCE and yield data, weather, soil, satellite indicators, and geospatial datasets into crop modelling and agricultural analytics workflows.
• Apply Python/R, statistical modelling, data analytics, and machine-learning techniques to develop scalable agricultural solutions.
• Collaborate with GIS, remote sensing, data science, engineering, and product teams to operationalize crop models and analytics.
• Translate scientific insights into dashboards, advisories, crop monitoring platforms, and decision-support systems.
• Lead technical engagements with clients, government agencies, research institutions, and agricultural stakeholders, converting requirements into practical and scientifically robust solutions.
• Support research, PoCs, pilots, proposals, technical documentation, and new agriculture/climate-tech initiatives, while mentoring junior technical teams.
  
  

Skills and Experience :

• 8+ years of relevant experience in agronomy, crop science, crop modelling, agricultural analytics, or climate-smart agriculture, with strong knowledge of crop physiology, phenology, crop calendars, crop management, and soil-water relationships.
• Strong expertise in DSSAT, APSIM, AquaCrop, WOFOST, or equivalent crop modelling platforms, including model parameterization, calibration, validation, sensitivity analysis, and uncertainty assessment.
• Experience in crop stress/risk assessment, drought and climate-impact analysis, and large-scale agricultural applications.
• Proficiency in Python and/or R, statistical modelling, data analytics, and machine learning, with experience handling agricultural datasets.
• Strong understanding of IMD, ERA5, CHIRPS, soil, CCE/yield, field, satellite, GIS, and remote-sensing datasets, including NDVI, LSWI, EVI, and related crop indicators.
• Strong technical leadership, problem-solving, stakeholder management, communication, and mentoring skills.
• Experience working with government agencies, research institutions, agriculture, or climate-tech organizations is preferred.
  
  

KPIs:

• Crop Model Performance: Accuracy, calibration, validation, and successful deployment of DSSAT, APSIM, AquaCrop, WOFOST, or equivalent models.

• Crop Intelligence & Risk: Effective crop monitoring, yield forecasting, stress, drought, climate-impact, risk, productivity, and loss assessment.
• Data Quality & Integration: Accurate integration and validation of field/CCE, yield, weather, soil, satellite, and geospatial data.
• Solution Delivery: Timely delivery of scalable crop intelligence, advisories, dashboards, and decision-support solutions.
• R&D & Innovation: Successful delivery of PoCs, research initiatives, new methodologies, and improvements in agricultural analytics.
• Technical & Client Leadership: Effective stakeholder engagement, technical decision-making, domain guidance, and client satisfaction.
• Cross-functional Collaboration: Successful coordination with GIS, remote sensing, data science, engineering, and product teams.
• Documentation & Compliance: Timely, accurate technical documentation, validation reports, proposals, and project deliverables.
• Team Development: Mentoring, knowledge sharing, and capability building within agronomy and crop-modelling teams.
• Project Delivery & Quality: Completion of technical deliverables within timelines while maintaining scientific and project standards.