falowo

Led the development of real-time model monitoring dashboards in Google Looker using SQL queries, and developed Python packages for model evaluation, ensuring proactive detection and mitigation of concept and data drift. Led performance analysis and profiling of machine learning inference workflow to resolve isolated bottlenecks resulting in a 40% improvement in inference workflow. Implemented data quality checks and data governance processes to ensure data accuracy and compliance. Optimised data augmentation processes within model training data pipeline utilising tensor flow functions, resulting in a 30% increase in data augmentation workflow. Led improvement of complex machine learning models used in interpreting reservoir data and making exploration decisions by introducing an AdamW optimizer and a custom learning rate scheduler, resulting in improvement of model generalisation.

Engineered high-volume ETL pipeline (millions of records daily) using Google Cloud Dataflow for orchestration, and Python/PySpark for data transformation. Leveraged BigQuery for analytics, achieving a 35% improvement in reporting efficiency. Documentation on data processing systems and procedures to ensure efficient data processing and analysis,resulting in a reduction of time required to onboard new team members by 30% with AWS Confluence and Google Cloud Docs. Developed cloud-native batch data processing systems on Google Compute Engine, achieving a 50% increase in processing speed leveraging Terraform for infrastructure provisioning, Docker for containerization, and Airflow for orchestration. Led stakeholder requirements gathering, translating insights into actionable business strategies. Created comprehensive documentation for efficient knowledge transfer, reducing onboarding time by 30%.

Led a team of 8+ IT experts to deploy disaster recovery systems as a fail-safe to prevent downtime across work branches and the head office. Improved operational efficiency by 30%.