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Director, Analytics Engineering (2 Openings)
Novartis
Remote, USA
Full-time
Remote
Discovered Today
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Major accountabilities:
• Design and implement intelligent, self-healing data pipelines that leverage AI/ML for automated data quality monitoring, anomaly detection, and remediation.
• Build and maintain centralized feature stores that enable feature reusability across multiple models and use cases.
• Create curated data repositories optimized for data science/AI workflows, including training datasets, evaluation datasets, and production serving layers.
• Develop automated feature engineering pipelines that transform raw data into analytics-ready features with lineage tracking.
• Partner with Enterprise IT to optimize analytics platform architecture for high-performance data science workloads.
• Build automated pipelines that integrate diverse data sources including sales, CRM, patient claims, real-world evidence, and unstructured data.
• Create self-service data access layers that empower data scientists and analysts to query and extract data independently.
• Establish SLAs for data availability, freshness, and quality; implement monitoring and observability solutions.
Full Job Posting
Job Description
Major accountabilities:
- Design and implement intelligent, self-healing data pipelines that leverage AI/ML for automated data quality monitoring, anomaly detection, and remediation.
- Build and maintain centralized feature stores that enable feature reusability across multiple models and use cases.
- Create curated data repositories optimized for data science/AI workflows, including training datasets, evaluation datasets, and production serving layers.
- Develop automated feature engineering pipelines that transform raw data into analytics-ready features with lineage tracking.
- Partner with Enterprise IT to optimize analytics platform architecture for high-performance data science workloads.
- Build automated pipelines that integrate diverse data sources including sales, CRM, patient claims, real-world evidence, and unstructured data.
- Create self-service data access layers that empower data scientists and analysts to query and extract data independently.
- Establish SLAs for data availability, freshness, and quality; implement monitoring and observability solutions.
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