Data Scientist

About Greenphyto

Greenphyto is a Singapore-based company specializing in agri-technology, focused on developing highly automated indoor vertical farming systems. Their approach integrates artificial intelligence, computer vision, robotics, environmental sensing, automation, and farm-management software designed to enhance crop yield, quality, traceability, and operational efficiency.

Role Overview

The Data Scientist position entails using operational, environmental, production, and imaging datasets to craft machine-learning and AI solutions that boost crop yield, plant quality, and overall farm efficiency. This role emphasizes computer vision, crop growth analytics, yield forecasting, anomaly detection, and optimal condition tuning. Collaboration with software engineers, agronomists, production, and automation staff is essential to convert farming challenges into scalable solutions.

Key Responsibilities

• Create machine-learning and statistical models targeting crop yield prediction, growth trends, and production scheduling.
• Develop computer-vision algorithms to assess and identify plant traits.
• Perform detailed analyses of environmental and operational data.
• Uncover correlations among growing conditions, management activities, crop quality, and harvest output.
• Establish models for anomaly detection, early warnings, and quality control monitoring.
• Manage dataset preparation including cleaning, labeling, and validation of various data types such as structured, time-series, and images.
• Design experiments and rigorously evaluate model accuracy with statistical and machine-learning techniques.
• Deploy models and analytics services within Greenphyto's farm-management and AI frameworks.
• Build APIs, data pipelines, dashboards, and automated reporting tools to facilitate operational decisions.
• Monitor deployed models for performance consistency, accuracy, data drift, and decline.
• Work in partnership with agronomists and production teams to validate findings through field trials.
• Maintain comprehensive documentation covering datasets, methodologies, assumptions, model specifics, and deployment practices.
• Stay informed on advances in computer vision, machine learning, precision agriculture, and controlled-environment farming.

Minimum Qualifications

• Bachelor's degree in Data Science, Computer Science, AI, Statistics, Mathematics, Engineering, or related fields.
• At least two years of professional experience in data science, machine learning, computer vision, or applied analytics.
• Strong Python programming proficiency.
• Hands-on experience with libraries like Pandas, NumPy, scikit-learn, PyTorch, or TensorFlow.
• Demonstrated skill in developing, assessing, and refining machine-learning models.
• Good grasp of statistical analysis, feature engineering, model validation, and performance metrics.
• Experience working with SQL and handling large or complex datasets.
• Effective communication skills to convey technical insights to both tech and operational personnel.
• Strong analytical and problem-solving capabilities.
• Readiness to engage with real farm data and support validation in a vertical-farm setting.

Preferred Qualifications

• Familiarity with computer vision tasks such as image segmentation, object detection, or classification.
• Experience with advanced models like CNNs, vision transformers, time-series forecasting, LSTM networks, or segmentation architectures.
• Knowledge in deploying models via APIs, containerization, or cloud/on-premise setups.
• Understanding of MLOps methodologies including versioning, experiment tracking, monitoring, and automation.
• Experience with tools such as Git, Docker, Linux, and RESTful APIs.
• Skills in processing data from IoT sensors, cameras, PLCs, robotics, or automation devices.
• Background in controlled-environment agriculture, plant sciences, horticulture, or precision farming.
• Exposure to cloud platforms like AWS, Azure, or Google Cloud.
• Competency with visualization tools including Power BI, Grafana, Plotly, or Tableau.

Core Competencies

• Pragmatic and results-driven data science approach.
• Ability to translate business and farm operational challenges into quantifiable analytical problems.
• Focus on data quality, reproducibility, and robust model performance.
• Capability to work autonomously while collaborating with cross-disciplinary teams.
• Curiosity about plant growth and farming production processes.
• Strong ownership and dedication to deploying reliable, production-ready solutions.