Principal Data Scientist

Where You'll Make an Impact:

• Significantly improve models that help farmers optimize irrigation, conserve water, and understand field conditions (e.g., rainfall, evapotranspiration, water balance).
• Advance Arable's predictive capabilities through the application of novel ML techniques and sensor data analysis.
• Contribute directly to tools supporting climate resilience and sustainable practices in agriculture.

What You Will Do:

• Lead End-to-End Model Development: Drive the full lifecycle of core machine learning models – from research, prototyping, and validation to deployment (Python, Docker, Flask, AWS/SageMaker) and ongoing performance monitoring and improvement. Key areas include water balance, ET, rainfall, and irrigation insights.
• Conduct Applied Research & Innovation: Identify opportunities and execute applied R&D projects to enhance model accuracy, leverage new data sources (internal sensor streams, external weather data), and develop novel predictive features, balancing exploration with pragmatic delivery.
• Collaborate for Impact: Work closely with cross-functional teams – Product (defining requirements, translating features), Sensors/IoT (understanding data, calibration and validation), and Software (API integration, production pipelines) – to ensure data science solutions effectively meet business and user needs.
• Ensure Solution Quality & Provide Expertise: Uphold high standards for model performance and data integrity through rigorous validation, anomaly detection, and addressing operational analytical needs. Serve as a subject matter expert in your domain areas and contribute to the team's technical strategy and best practices, potentially mentoring junior members.

Required Experience and Skills:

• MS or PhD in a quantitative field or equivalent deep practical experience.
• 5-8+ years relevant hands-on experience developing & deploying ML/DS solutions.
• ML & Statistical Depth: Strong theoretical understanding and practical expertise in machine learning (especially time-series), statistical modeling, and validation techniques.
• R&D Acumen: Demonstrated ability to conduct applied research, tackle ambiguous problems, and deliver impactful, data-driven solutions.
• Technical Implementation: Proficiency in Python for data science (NumPy, pandas, scikit-learn, etc.), strong software engineering practices (Git, testing, docs), and experience deploying models via APIs (Flask) using containers (Docker) on cloud platforms (AWS).
• Communication & Collaboration: Excellent ability to communicate complex concepts clearly and collaborate effectively within a cross-functional environment.

Preferred Experience and Skills:

• Domain Knowledge: Background or strong interest in agriculture, hydrology, meteorology, soil science, or related environmental sciences.
• Sensor Data & Techniques: Experience with real-world IoT sensor data (including CalVal), anomaly detection, and leveraging external datasets (weather, geospatial).
• Startup Environment: Proven ability to thrive and take ownership in a fast-paced, dynamic startup setting.
• AWS ML Ecosystem: Deep familiarity with AWS services, particularly SageMaker.
• Mentoring: Experience guiding or mentoring other technical team members.