Data Scientist

About This Role

We are looking for a Data Scientist to build machine learning models that power emissions prediction, anomaly detection, and compliance risk scoring across the GreenLedger platform. You will work with large-scale emissions datasets from enterprises across the ASEAN region, developing algorithms that help organizations understand their carbon footprint and anticipate compliance requirements. This is a unique opportunity to apply data science to one of the most pressing challenges of our time.

Responsibilities

• Develop and deploy machine learning models for emissions forecasting and trend prediction
• Build anomaly detection systems that flag unusual patterns in emissions data for compliance review
• Design and implement compliance risk scoring algorithms based on regulatory thresholds and historical data
• Analyze large-scale emissions datasets to identify industry benchmarks and patterns
• Create data pipelines for feature engineering, model training, and inference at scale
• Collaborate with engineering teams to integrate ML models into production applications
• Develop and maintain model monitoring dashboards to track performance and drift
• Research and evaluate emerging ML techniques applicable to carbon accounting and climate data
• Present findings and recommendations to product and leadership teams

Requirements

• Master or PhD in Computer Science, Statistics, Data Science, or a related quantitative field
• Strong proficiency in Python and data science libraries including pandas, scikit-learn, and PyTorch or TensorFlow
• Experience with time-series analysis and forecasting methodologies
• Solid foundation in statistics, probability, and experimental design
• Experience deploying ML models in production environments
• Familiarity with cloud ML platforms such as AWS SageMaker, Azure ML, or GCP Vertex AI
• Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders

Nice to Have

• Experience with environmental data, climate modeling, or sustainability metrics
• Knowledge of GHG Protocol emissions accounting methodologies
• Experience with natural language processing for regulatory document analysis
• Familiarity with MLOps practices and tools such as MLflow, DVC, or Kubeflow
• Published research in relevant areas of machine learning or environmental science