Data scientist_Databricks

Job Description

Role Summary

\n 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.

\n Key Roles & Responsibilities

\n\n • Understand business problems and translate them into data science, predictive analytics, or machine learning use cases.

\n • Explore, clean, transform, and validate structured and unstructured data from multiple data sources.

\n • Develop statistical, machine learning, and predictive models using Python and Databricks.

\n • Build end-to-end ML pipelines in Databricks for data preparation, feature engineering, model training, evaluation, and deployment.

\n • Use Databricks capabilities such as notebooks, Spark, Delta Lake, MLflow, and Databricks Workflows for scalable model development and lifecycle management.

\n • Evaluate model performance using appropriate metrics; perform tuning, validation, and error analysis.

\n • Collaborate with Data Engineers to ensure reliable data ingestion, data quality, feature availability, and production-ready pipelines.

\n • Support model deployment through APIs, batch scoring, or integration with downstream applications and reporting platforms.

\n • Implement model monitoring for performance drift, data drift, accuracy, bias, and retraining requirements.

\n • Document datasets, assumptions, model logic, performance results, limitations, and deployment/release details.

\n • Present findings, insights, and recommendations clearly to business and technical stakeholders.

\n • Follow data governance, security, privacy, and responsible-AI requirements applicable to the client environment.

\n • Participate in code reviews, peer reviews, agile ceremonies, and continuous improvement initiatives.

Essential

Skills /

\n\nExperience

\n\n • 4–8 years of experience in Data Science, Machine Learning, Advanced Analytics, or related roles.

\n • Strong hands-on experience with Databricks and Apache Spark/PySpark.

\n • Strong Python experience, including Pandas, NumPy, Scikit-learn and relevant ML libraries.

\n • Experience developing classification, regression, clustering, forecasting, NLP, or recommendation models.

\n • Experience with SQL and large-scale data processing.

\n • Hands-on experience with MLflow, model versioning, experiment tracking, and model deployment is preferred.

\n • Understanding of Delta Lake, data lake/lakehouse concepts, and cloud platforms such as Azure, AWS, or GCP.

\n • Experience with Git, CI/CD, APIs, and MLOps practices is desirable.

\n • Good stakeholder communication and ability to explain model outcomes in simple business terms. Preferred / Good to Have

\n • Databricks certification.

\n • Experience with Azure Databricks, Azure ML, AWS SageMaker, or similar platforms.

\n • Experience in regulated industries, healthcare, medical devices, pharma, or data privacy-sensitive environments.

\n • Knowledge of Responsible AI, explainability, bias assessment, and model governance.

\n • Experience with Power BI/Tableau for insight visualization.

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