What We're Looking For:

Arable Labs is seeking a scientifically-minded and skilled Senior Data Scientist based in Mexico to join our globally distributed team. We are looking for an individual with a strong research background to apply deep expertise in machine learning, statistical analysis, and physics-based modeling to solve complex challenges in agricultural water management. Your work will focus on modeling atmospheric processes and field-level hydrology to deliver critical insights for farms. If you are passionate about applying your scientific skills to tangible environmental problems and thrive in a remote, collaborative setting, this role is for you.

What We Do:

At Arable, our mission is to accelerate the adoption of sustainable agriculture. Our integrated hardware-software solution empowers growers to make more informed decisions, manage resources like water sustainably, and adapt to climate change. We believe reliable, hyper-local data is the foundation for a more resilient and productive agricultural future.

Where You'll Make an Impact:

Develop and improve spatio-temporal models of atmospheric processes to help farmers optimize water use for both pivot and flood irrigation systems.
Advance Arable's predictive capabilities through the application of novel ML techniques and sensor data analysis.
Contribute directly to tools that support climate resilience and sustainable water management practices in agriculture.

What You Will Do:

Own End-to-End Model Development: Take ownership of the full lifecycle of predictive models, from research and prototyping to deployment and monitoring, using a blend of machine learning, statistical, and physics-based approaches.
Execute Applied Research: Contribute to applied R&D projects to enhance model accuracy, leverage new data sources (including remote sensing and geospatial data), and develop novel predictive features.
Collaborate for Impact: Work closely with our cross-functional teams in Product, Sensors, and Software to ensure data science solutions effectively meet user and business needs.
Ensure Scientific Rigor: Uphold high standards for model performance and data integrity through rigorous validation and analysis, contributing to the team's technical best practices.

Experience and Skills:

Required
BS in a quantitative or scientific field (e.g., Physics, Atmospheric Science, Environmental Science, Engineering, Computer Science).
4+ years of hands-on experience developing and deploying data-driven models in a commercial or research setting.
English Proficiency: Professional working proficiency in English (written and verbal) is required for collaboration in our globally distributed team.
Modeling Depth: Strong expertise in building and validating predictive models using machine learning, statistical, or physics-based methods.
Technical Implementation: Proficiency in Python for data science (e.g., pandas, NumPy, scikit-learn, SciPy), strong software engineering practices (Git, testing), and experience deploying models using containers (Docker) on cloud platforms (AWS).
Global Collaboration: Proven ability to communicate and collaborate effectively in a highly distributed team across significant time zone differences.
Preferred
MS or PhD in a relevant scientific field.
Domain Knowledge: Background in agronomy, hydrology, atmospheric science, or environmental science.
Data Experience: Experience working with remote sensing, atmospheric, or geospatial datasets.
Startup Environment: Ability to thrive and take ownership in a fast-paced, dynamic startup setting.

Location

Remote within Mexico. Travel to the US and other locations once per quarter at most.

What We Offer:

Join a dedicated team at Arable using cutting-edge technology to build a more sustainable future. We foster a culture of curiosity, impact, and collaboration.

A competitive local compensation package.
Comprehensive benefits in accordance with local standards.
The flexibility of a remote work environment.
The opportunity to see your work create a tangible positive impact for growers and the environment.