Senior Engineer - Data Science

• OCI Data Science Platform: Build, train, evaluate and deploy machine learning models using OCI Data Science, notebooks and Python-based ML solutions.

• Data Preparation & Feature Engineering: Perform data cleansing, feature engineering, dataset management and model validation activities.
• Machine Learning & Analytics: Develop supervised and unsupervised models including forecasting, classification, clustering and recommendation solutions.
• Model Lifecycle & Governance: Manage model lifecycle, versioning, approvals, governance and traceability through OCI Model Catalog or equivalent controls.
• MLOps & Deployment: Deploy models as scalable REST API endpoints and support CI/CD pipelines for model promotion and production deployment.
• Monitoring & Continuous Improvement: Monitor model performance, utilisation and drift and support retraining and optimisation activities.
• Operational Support: Provide L2/L3 support for deployed AI/ML solutions, troubleshooting, enhancements and knowledge base updates. Primary Skills:
• OCI Data Science
• Python
• SQL
• Machine Learning
• Model Validation
• Statistical Analysis Secondary/ Support Skills:
• OCI DevOps
• Docker
• Kubernetes
• Data Engineering concepts
• AI Governance
• Model monitoring Tools/ Platforms:
• Jupyter Notebooks
• Tensor. Flow
• Py. Torch
• Scikit-learn
• OCI Object Storage
• Oracle Autonomous Database
• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering or a related field.
• Minimum 5 years of experience in data science, machine learning, advanced analytics or predictive modelling.
• Minimum 3 years of experience using OCI Data Science or equivalent cloud ML platforms.
• Experience developing, training, deploying and monitoring machine learning models in production environments.
• Strong understanding of MLOps, feature engineering, model governance and large-scale data analysis.
• OCI Data Science Professional, OCI AI Foundations Associate or OCI AI Professional certifications are preferred.