Data Scientist

• Engage in all phases of the data science lifecycle: problem framing, exploratory data analysis, feature engineering, modeling, evaluation, deployment, and monitoring, with guidance from senior team members.
• Develop robust Python notebooks, jobs, and pipelines (using pandas, scikit-learn, PySpark) on Databricks (Delta Lake, Unity Catalog, MLflow, Jobs/Workflows).
• Implement testing and quality checks, including unit tests, data validation, and drift/bias monitoring; maintain reproducible experiments and clear documentation.
• Participate in code and design reviews, use git and CI/CD, and manage technical debt.
• Clarify requirements, propose iterative solutions, and communicate findings to both technical and non-technical stakeholders.
• Collaborate with platform and governance teams to ensure security, lineage, and auditability.

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