• Identify the right model for every use case - simple empirical model to a deep learning based model and everything in between
• Devise strategies to test and trial run the models built
• Continuously monitor the performance of models and enhance
• Own the entire process end to end from data collection, cleaning, feature engineering, algorithm/tool selection, model testing to the final model
• Translate data models into efficient engineering code to deploy at scale efficiently