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 straightforward 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.

📌 Data scientist_Databricks (Chennai)
🏢 Archimedis Digital
📍 Chennai