Lead the design, development, and deployment of machine learning and statistical models to solve real-world business problems. Collaborate with cross-functional teams (Product, Engineering, Business) to understand business needs, define project scopes, and translate them into data science problems. Develop, test, and productionize models using Python, SQL, and PySpark on large-scale datasets. Apply deep learning and advanced statistical modeling techniques for use cases such as classification, regression, recommendation, and personalization. Perform feature engineering, data wrangling, model selection, hyperparameter tuning, and model evaluation. Monitor model performance and ensure robust ML lifecycle management using tools like MLflow and Airflow. Document processes and present insights, findings, and recommendations to both technical and non-technical stakeholders. Stay current with new trends in ML/AI and proactively apply emerging techniques where appropriate.