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/Py Spark. Strong Python experience, including Pandas, Num Py, 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 Sage Maker, 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.