Data Scientist

About Greenphyto

Greenphyto is a Singapore-based agri-technology company developing highly automated indoor vertical farming solutions. Our operations combine artificial intelligence, computer vision, robotics, environmental sensors, automation systems and farm-management software to improve crop yield, quality, traceability and operational efficiency.

We are looking for a Data Scientist to develop and deploy data-driven solutions for commercial farming operations.

Role Overview

The Data Scientist will work with operational, environmental, production and image data to develop machine-learning and AI solutions that improve crop yield, plant quality and farm productivity.

The role will focus on areas such as computer vision, crop-growth analysis, yield prediction, anomaly detection and optimisation of growing conditions. The successful candidate will work closely with software engineers, agronomists, production teams and automation engineers to translate operational challenges into practical, production-ready solutions.

Key Responsibilities

. Develop machine-learning and statistical models for crop yield prediction, growth forecasting and production planning.

. Build computer-vision models to measure and identify plant characteristics.

. Analyse environmental and operational data.

. Identify relationships between growing conditions, operational activities, crop quality and harvest yield.

. Develop models for anomaly detection, early warning and production-quality monitoring.

. Prepare, clean, label and validate structured, time-series and image datasets.

. Design appropriate experiments and evaluate model performance using suitable statistical and machine-learning methods.

. Deploy models and analytical services into Greenphyto's farm-management and AI platforms.

. Develop APIs, data pipelines, dashboards and automated reports to support operational decision-making.

. Monitor deployed models for accuracy, reliability, data drift and performance degradation.

. Collaborate with agronomists and production teams to validate model findings through farm trials.

. Maintain clear documentation covering datasets, experiments, models, assumptions, limitations and deployment procedures.

. Keep up to date with developments in computer vision, machine learning, precision agriculture and controlled-environment agriculture.

Minimum Requirements

. Bachelor's degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering or a related discipline.

. At least 2 years of relevant professional experience in data science, machine learning, computer vision or applied analytics.

. Strong programming skills in Python.

. Practical experience with data-processing and machine-learning libraries such as Pandas, NumPy, scikit-learn, PyTorch or TensorFlow.

. Experience developing, evaluating and improving machine-learning models.

. Good understanding of statistical analysis, feature engineering, model validation and performance metrics.

. Experience working with SQL databases and large or complex datasets.

. Ability to communicate technical findings clearly to both technical and operational stakeholders.

. Strong analytical thinking and problem-solving skills.

. Willingness to work with real-world farm data and conduct validation activities within an operational vertical-farm environment.

Preferred Qualifications

. Experience with computer vision, image segmentation, object detection or image classification.

. Experience with models or techniques such as CNNs, vision transformers, time-series forecasting, LSTM networks or segmentation models.

. Experience deploying models through APIs, containers or cloud/on-premise environments.

. Familiarity with MLOps practices, including version control, experiment tracking, model monitoring and automated deployment.

. Experience with Git, Docker, Linux and REST APIs.

. Experience processing data from IoT sensors, cameras, PLCs, robotics or automation systems.

. Knowledge of controlled-environment agriculture, plant science, horticulture or precision farming.

. Experience with cloud platforms such as AWS, Azure or Google Cloud.

. Familiarity with data-visualisation tools such as Power BI, Grafana, Plotly or Tableau.

Key Competencies

. Practical and results-oriented approach to data science.

. Ability to translate business and operational challenges into measurable analytical problems.

. Attention to data quality, model reliability and reproducibility.

. Ability to work independently while collaborating across multidisciplinary teams.

. Curiosity and willingness to understand plant-growth and farm-production processes.

. Strong ownership and commitment to delivering solutions that work reliably in production.