Project Manager/Scrum Master – Data & Analytics
Job Description
Agile Delivery & Servant Leadership
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Serve as the hands-on Scrum Master for one or more teams, facilitating all core Agile ceremonies (sprint planning, daily stand-ups, retrospectives), ensuring they are purposeful and outcome-oriented.
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Remove delivery impediments by coordinating proactively across IT, Security, and business partners.
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Coach Product Owners and Engineers on Agile principles, effective user story development, backlog refinement, and flow-based metrics (throughput, cycle time).
AI/ML Ops Implementation
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Drive the adoption of AI/ML Ops practices, including version control, CI/CD for data and models, automated testing, and monitoring.
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Partner with architecture leads to align team practices with modern data platform patterns (e.g., Databricks Lakehouse, RAG, agentic automation).
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Ensure the "Definition of Done" includes operationalization requirements like observability, lineage, and documentation.
Governance, Compliance & Quality
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Ensure delivery practices support regulatory expectations (GxP/SOX) for data and AI in a life sciences context, including validation and audit trails.
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Partner with Data Governance and Security to embed required controls (policy-as-code) directly into team workflows.
-
Coordinate multiple functions (IT, regulatory, operations, vendors) to provide effective project delivery leadership.
Agile Delivery & Servant Leadership
-
Serve as the hands-on Scrum Master for one or more teams, facilitating all core Agile ceremonies (sprint planning, daily stand-ups, retrospectives), ensuring they are purposeful and outcome-oriented.
-
Remove delivery impediments by coordinating proactively across IT, Security, and business partners.
-
Coach Product Owners and Engineers on Agile principles, effective user story development, backlog refinement, and flow-based metrics (throughput, cycle time).
AI/ML Ops Implementation
-
Drive the adoption of AI/ML Ops practices, including version control, CI/CD for data and models, automated testing, and monitoring.
-
Partner with architecture leads to align team practices with modern data platform patterns (e.g., Databricks Lakehouse, RAG, agentic automation).
-
Ensure the "Definition of Done" includes operationalization requirements like observability, lineage, and documentation.
Governance, Compliance & Quality
-
Ensure delivery practices support regulatory expectations (GxP/SOX) for data and AI in a life sciences context, including validation and audit trails.
-
Partner with Data Governance and Security to embed required controls (policy-as-code) directly into team workflows.
-
Coordinate multiple functions (IT, regulatory, operations, vendors) to provide effective project delivery leadership.