We are reducing agriculture's global greenhouse gas emissions to improve the sustainability of food production. 

Agoro Carbon Alliance addresses a key global problem, the impact of agriculture's global greenhouse gas emissions on our climate and the implementation of sustainable carbon capturing potential at the farm and ranch. Our passion lies in solving a global crisis by working with our customers to bring about the sustainable transformation of agricultural practices which is both economically viable for the grower and makes the world avoid a crisis. 

A bit about the role

As a Geospatial Statistician within Agoro Carbon Alliance US, you and your team will be the lynchpin of our mission to bring about meaningful change to sustainable practices in farming and ranching around carbon sequestration. You and your team will design, implement, and evolve a foundation crucial to our success over time. This foundation will include designing, learning, and implementing pipelines that collect and process terabytes of remotely sensed imagery, climate data, soil data, and other spatial and non-spatial data. The data will be interpreted, analyzed, and modeled for estimating carbon emission and predicting the effect of these practices on soil health and much more in the US, Brazil, and other potential global geographies. In addition, you and your team will build and improve our carbon quantification workflows to scale, grow, and evolve as we grow. This is a journey of discovery; there is much to learn but together, with you in our team, we can figure it out. 

What you will do: 

• Design and implement a framework for assessing the accuracy and quality of landscape scale data products (crop type, agricultural practice adoption, etc)
• Design data collection strategies with the main techniques used in actual sampling practice, such as stratification, systematic selection, cluster sampling, and multistage sampling.
• Analyze complex survey data by taking into account the sample design, such as stratified random design, with different choices of variance estimation methods, to make inferences about the population as a whole.
• Run power and sample size analysis to optimize a survey sampling plan to collect samples with the minimum sample size, maximum efficiency, significant results with adequate power, the desired precision, and lower cost.
• Make geospatial predictions and associated standard error estimates at unsampled locations with the measurements at given spatial locations by using both spatial and non spatial dependent approaches. The applied tools could include SAS procedures of VARIOGRAM, KRIGE2D, and SIM2D, and ArcGIS Geostatistical Analyst Tools, Spatial Statistics Tools, and Spatial Analyst Tools.
• Statistically analyze data using mixed models containing fixed and random effects, with the mixed modeling procedures provided in SAS, such as GLM, GLIMMIX, and VARCOMP. 
• Act as subject matter expert to provide advice and guidance to other teams in the area of statistical support and development through an array of advanced statistical methods. Advocate for practical, relevant, and scientifically valid solutions.
• Develop automated data validation and review frameworks for machine learning based data products. 
• Contribute across the full product life-cycle: conduct R&D on potential focus areas, develop proof of concept data products for new offerings, and iterate on existing models to improve core products. 

Qualifications:

• Advanced degree or equivalent experience in Statistics, Data Science, Computer Science, or a related field.
• Have a robust statistics and data science background, in particular, deep understanding of the theory and principle of classic statistics, sampling surveys, experimental design, geostatistics, and other data modeling and analysis methods, including machine learning and artificial intelligence.
• Knowledgeable in inferential statistical principles and procedures, including regression, analysis of variances, power and sample size analysis, clustering, discriminant analysis, standardization, mixed modeling, categorical data analysis, and outlier detection. 
• Conceptual understanding of relational databases and proficiency in creating and maintaining project databases, developing data validation programs, and providing data cleaning and validation solutions in accordance with project data requirements and specifications.
• Five years of experience as a statistician in business associated with agriculture and natural resources and responsible for programming in SAS and other data analysis programming tools, including ArcGIS, to process spatial and non-spatial data from various sources and generate data reports.
• Understanding geospatial soil data and taking classes in GIS, remote sensing, and soil science are a plus.
• Demonstrate skills to use other programs or libraries, such as R, NumPy, SciPy, SKLearn, etc.
• Excellent oral and written communication skills

Desirable Skills: (bonus if you have them):

• Some familiarity with global agricultural systems is bonus