Senior Data Scientist

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October 29

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Arable

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Agriculture • SaaS • Sustainability

Arable is a company focused on providing advanced solutions for agriculture technology. They offer a comprehensive suite of tools, including sensing and monitoring systems, agronomic models, and applications to optimize irrigation, manage weather risks, and enhance crop management. Their technology ensures sustainability and efficiency in the agricultural sector, offering easy setup and reliable data for growers, advisors, researchers, and breeders. By collaborating with organizations like Google on water sustainability projects, Arable is recognized as a leading company focused on sustainability in agriculture.

51 - 200 employees

🌾 Agriculture

☁️ SaaS

📋 Description

• 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. • 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.

🎯 Requirements

• 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.

🏖️ Benefits

• 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