Data Scientist - Databricks + Azure MLflow

LTM Pan India Location Immediate joiners preferred Must have skill / Mandatory - Data Science, Python, SQL, PySpark, Databricks, Azure MLflow Data Scientist to develop advanced analytical solutions and machine learning models that drive business value

focuses on leveraging data applying statistical techniques and building scalable ML models using modern platforms such as Databricks MLflow and Azure ML

Machine Learning Model Development • Develop train and deploy machine learning models using Python Databricks Unity Catalog MLflow and workflows • Design and implement endtoend ML solutions including data preparation feature engineering model training and evaluation • Apply advanced algorithms across supervised unsupervised and deep learning use cases • Optimize model performance through hyperparameter tuning model selection and validation techniques Data Engineering Feature Development • Perform data extraction transformation and analysis using SQL and PySpark • Create and refine features from raw datasets to improve model accuracy and predictive power • Ensure data quality by processing cleansing and validating datasets Analytics Insights • Conduct exploratory data analysis EDA and data mining to uncover patterns trends and actionable insights • Develop analytical models to support strategic and operational decisionmaking • Perform ad hoc analysis and respond to business data requests Model Deployment Lifecycle • Deploy models using modern ML platforms Databricks Azure ML • Maintain and enhance existing models ensuring performance and reliability • Collaborate with engineering teams for scalable production deployment and monitoring Collaboration Communication • Work with crossfunctional stakeholders to identify AIMLdriven opportunities • Translate business problems into analytical solutions • Communicate findings through presentations dashboards and executive summaries Documentation Governance • Maintain clear documentation of models code and analytical processes • Ensure reproducibility and adherence to data governance standards