Machine Learning Specialist

Tasks

• Architect reproducible ML pipelines
• Build APIs for model outputs
• Build model validation and sign off reports
• Collaborate on field data quality
• Conduct model governance reviews
• Defend model methodology to partners
• Deliver analytics via SMS and WhatsApp
• Deploy production models
• Design ground truth data collection strategy
• Document model development
• Enforce model change protocol
• Ensure model version control
• Establish code quality and peer review
• Integrate field surveys
• Lead model development
• Manage crop cut sampling
• Mentor data science team members
• Monitor model performance
• Oversee drone imagery validation
• Own machine learning lifecycle
• Perform data quality assurance
• Present findings to non-technical stakeholders
• Track model drift and accuracy
• Train and validate models
• Translate model outputs to dashboards

Perks/Benefits

• N/A

Skills/Tech-stack

AWS | CNN | Convolutional Neural Networks | Data Integrity | Data Quality | Data quality assurance | Deployment Automation | Drift monitoring | Drone Imagery | EC2 | ECS | Ensemble Methods | Ensemble learning | Experiment tracking | Foundation Models | Geospatial machine learning | Git | Ground Truth | Ground-truth data | IAM | KoboToolbox) | LSTM | MLOps | Machine Learning | Model Drift | Model Governance | Model Registry | Model Validation | Model drift monitoring | Neural Networks | Prophet | Quality Assurance | Remote Sensing | Reproducible pipelines | S3 | Satellite imagery | Series modeling | Test Holdout | Time Series | Time Series Modeling | Truth data | Vegetation indices | Version control | XGBoost