Predictive Modeling: Build and deploy traditional Machine Learning (ML) solutions to help solve business problems.
Modeling Best Practices: Implement model explainability, rigorous experimentation, prompt engineering, feature engineering, cross-validation, hyperparameter tuning, bias detection, and robust MLOps deployment workflows.
Operational Excellence: Maintain comprehensive technical documentation, track historical requirements and design changes, enforce strict model versioning (data/code/weights), and implement reproducible research standards.
Data Preparation: Perform exploratory data analysis (EDA) and data wrangling on complex datasets.
Production Integration: Collaborate with engineering teams to integrate models into live systems.
(Preferred) Statistical Analysis: Apply fundamental statistics to validate models and run experiments.
