Data scientist_Databricks

Role Summary
We are seeking a Data Scientist with strong Databricks and machine learning experience to develop, deploy, and monitor data-driven models. The role will work closely with business, data engineering, and technology teams to convert business problems into scalable analytics and AI/ML solutions.
Key Roles & Responsibilities
• Understand business problems and translate them into data science, predictive analytics, or machine learning use cases.
• Explore, clean, transform, and validate structured and unstructured data from multiple data sources.
• Develop statistical, machine learning, and predictive models using Python and Databricks.
• Build end-to-end ML pipelines in Databricks for data preparation, feature engineering, model training, evaluation, and deployment.
• Use Databricks capabilities such as notebooks, Spark, Delta Lake, MLflow, and Databricks Workflows for scalable model development and lifecycle management.
• Evaluate model performance using appropriate metrics; perform tuning, validation, and error analysis.
• Collaborate with Data Engineers to ensure reliable data ingestion, data quality, feature availability, and production-ready pipelines.
• Support model deployment through APIs, batch scoring, or integration with downstream applications and reporting platforms.
• Implement model monitoring for performance drift, data drift, accuracy, bias, and retraining requirements.
• Document datasets, assumptions, model logic, performance results, limitations, and deployment/release details.
• Present findings, insights, and recommendations clearly to business and technical stakeholders.
• Follow data governance, security, privacy, and responsible-AI requirements applicable to the client environment.
• Participate in code reviews, peer reviews, agile ceremonies, and continuous improvement initiatives.
Essential Skills / Experience
• 4–8 years of experience in Data Science, Machine Learning, Advanced Analytics, or related roles.
• Strong hands-on experience with Databricks and Apache Spark/PySpark.
• Strong Python experience, including Pandas, NumPy, Scikit-learn and relevant ML libraries.
• Experience developing classification, regression, clustering, forecasting, NLP, or recommendation models.
• Experience with SQL and large-scale data processing.
• Hands-on experience with MLflow, model versioning, experiment tracking, and model deployment is preferred.
• Understanding of Delta Lake, data lake/lakehouse concepts, and cloud platforms such as Azure, AWS, or GCP.
• Experience with Git, CI/CD, APIs, and MLOps practices is desirable.
• Good stakeholder communication and ability to explain model outcomes in simple business terms.
Preferred / Good to Have
• Databricks certification.
• Experience with Azure Databricks, Azure ML, AWS SageMaker, or similar platforms.
• Experience in regulated industries, healthcare, medical devices, pharma, or data privacy-sensitive environments.
• Knowledge of Responsible AI, explainability, bias assessment, and model governance.
• Experience with Power BI/Tableau for insight visualization.