- Translate business problems into analytical solutions, identifying opportunities for predictive modeling, optimization, and data-driven decision-making
- Design, develop, and deploy machine learning models using techniques such as classification, regression, clustering, and forecasting
- Apply statistical methods and experimentation techniques (hypothesis testing, A/B testing) to validate models and insights
- Conduct exploratory data analysis (EDA) to identify patterns, trends, and key drivers within large datasets
- Engineer features and prepare datasets to improve model performance and robustness
- Evaluate and optimize models using appropriate metrics, cross-validation, and tuning strategies
- Ensure model explainability and interpretability, communicating results clearly to both technical and non-technical stakeholders
- Design and implement MLOps practices including model versioning, monitoring, and retraining strategies
- Collaborate with data engineers to access, prepare, and scale datasets from Azure-based platforms
- Present insights and recommendations through compelling storytelling and data visualization
PythonSQLMachine Learning+5 more