Data Scientist, Soil Carbon Modeling

We are reducing agriculture’s global greenhouse gas emissions to improve the world we live in!

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 ranch and farm. Our passion lies in solving a global crisis by working with our customers to bring about the sustainable transformation of ranching and farming practices which is both economically viable for the grower and helps the world avoid a crisis.

A bit about the role:

Our team brings together knowledge in (geo-)statistics, agriculture, soil and carbon science, as well as applied data science to support Agoro’s monitoring, reporting and verification (MRV) process of greenhouse gas emissions - with a primary focus on soil carbon sequestration.

As a data scientist - within our Carbon Science and analytics team, you will contribute to the development of robust carbon modelling approaches, in the context of the design and implementation of:

• stratification approaches, for physical soil sampling purposes
• a cost-effective monitoring approach of soil organic carbon (SOC) stock, resulting from various sustainable management practices.

Your work will drive several aspects of Agoro’s core business, for instance:

• assess soil sequestration potential and prioritize areas for business development
• optimize carbon credit claims, by ensuring robustness and traceability of the “carbon journey”
• decrease the cost of SOC quantification by the innovative combination of different approaches

Summary

You will combine your expertise in deploying existing carbon models, such as DayCent, DNDC, etc. with the use of other approaches, gradually including remote and proximal sensing data for carbon monitoring. To do that, you will work closely with remote sensing experts. You will be expected to leverage a wide range of datasets, going all the way from global climate data to local ranch or farm/field data. Your models will span across various ranges of spatial, temporal and spectral resolution. You’ll be expected to be able to handle these independently. Carbon crediting resulting from the implementation of sustainable management practices in farms and ranches, evolves in a rapidly changing “regulatory” framework. Your agility and eagerness to continuously suggest and develop innovative ideas will therefore be an important feature of your profile. You will be working with our data engineering team to build your prototypes to production and running on scale.

What you will be doing

• The preferred candidate will support building and analysing ecosystem model datasets, using state-of-the-art computing frameworks/algorithms to help calibrate/validation process-based models, and be able to tune your models efficiently.
• You would primarily contribute to our current modeling effort, develop and implement algorithms that will help improve carbon simulation models and support measurement, reporting and verification of soil carbon stock changes and trace gas emission reductions.
• You would be encouraged to innovate solutions for more efficient high-throughput processing of survey data into model inputs. The successful candidate will thrive on meeting challenges, insist on high quality deliverables, demonstrate effective problem-solving skills, and possess the ability to build productive external and internal relationships.
• Collect, compile, and analyze datasets relevant for soil carbon modeling for agriculture and rangeland applications; build model inputs, analyze simulation results, identify possible issues if any and provide insight into simulated soil organic carbon dynamics.
• Use specialized knowledge and current literature when identifying and implementing process improvement; bring academic research, technology and practical experience together to drive Agoro’s soil modeling strategy forward.
• Rapidly prototype and iterate to build production ready solutions that scale.

 What you will bring:

• Bachelor’s degree required; MS/PhD in Agriculture/Environmental science/Soil science, or Applied Math, Statistics preferred.
• Minimum of 3 years’ experience working with environmental, soil carbon modeling systems, or terrestrial modeling.
• Expertise in working with relevant soil carbon models (e.g., DayCent, DNDC) is critical.
• Knowledge of processes related to soil carbon sequestration and process-based models (i.e, DayCent, DNDC, APEX, SWAT-C etc.) and their requirements.
• Excellent analytical skills to find solutions to meet business needs.
• Ability to work effectively as a part of a team.
• Strong intention to learn and contribute to the success of the team.
• Highly proficient and experienced in scripting languages such as Python and R, statistical and modelling packages, and rapid prototyping, with ability to write clean, sharable and efficient code.
• You are an effective communicator of technical concepts to both technical and non-technical resources.
• Pragmatic, solution driven and technology agnostic. You focus on results, and get things done.
• Prior agricultural experience is a plus, especially in a commercial context.
• Fluent in English
• Fluent in Portuguese is a plus, not required. 

 What will set you apart:

• You have experience using Machine Learning in modeling frameworks.
• You are competent at, and enjoy developing analytical solutions/tools, producing value-added datasets, writing up and reporting results.
• You have experience developing process-based ecosystem models.
• You have a solution-oriented mindset.

Why work with us?

• We offer the opportunity to drive change by globally reducing carbon emissions while financially supporting growers.
• You would be working with a globally dispersed and diverse team. We adopt a virtual-first approach, where we encourage face-to-face collaboration, but are focused on recruiting the best talent.
• Support for personal development, learning and continuous learning is a priority.

Additional Information

As a global organisation we actively strive to reflect the diversity in society. We therefore encourage all qualified applicants from all backgrounds to apply and are committed to creating a work environment that fits gender equality and allows combining career progress with the needs of a family or other personal circumstances.