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1 Vacancy
Key Responsibilities:
Monitor the health and performance of production data science systems including predictive models dashboards and data pipelines.
Diagnose issues such as data drift performance degradation or infrastructure instability and implement timely fixes.
Automate monitoring tasks and health checks related to data quality forecast accuracy and pipeline execution.
Update and patch environments and applications ensuring smooth operation across versions and dependencies.
Collaborate with engineers and data scientists to refactor and optimize code for long-term maintainability.
Maintain detailed documentation and change logs to ensure knowledge sharing and traceability.
Support incident response including root cause analysis and post-incident improvements.
Ensure compliance with all applicable data privacy security and regulatory standards.
Requirements:
Bachelors degree in Computer Science Engineering Mathematics or a related field.
Minimum 2 years experience in a data engineering MLOps or system maintenance role.
Solid understanding of data science production workflows including pipelines and model lifecycle.
Proficient in Python and R with strong debugging and refactoring capabilities.
Confident in SQL and managing large-scale datasets in production.
Experience with CI/CD Git and containerization tools like Docker.
Familiarity with cloud infrastructure (AWS GCP or Azure) and DevOps best practices.
Strong analytical problem-solving and communication skills.
Full-Time