Serve as the Scrum Master for one or more Data & Analytics squads delivering use cases such as patient finding, trial optimization, and AI-assisted authoring.
Facilitate all core Agile ceremonies (sprint planning, daily stand-ups, retrospectives) ensuring they are purposeful and outcome-oriented.
Remove delivery impediments by coordinating across IT, security, and business partners.
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 "Definition of Done" includes operationalization requirements like observability, lineage, and documentation rather than just the initial build.
Coach Product Owners, Engineers, and Analysts on Agile principles, story writing, and flow-based metrics (throughput, cycle time, WIP).
Promote consistent use of IT Product Centric Framework (ITPCF) patterns and Jira standards.
Foster a culture of psychological safety, turning retrospective insights into concrete action items.
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.

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field (Advanced degree or certifications like CSM, PMI-ACP, or SAFe preferred).
5–8+ years in Agile delivery roles supporting data, analytics, or software engineering teams.
Hands-on experience implementing DevOps or MLOps practices (CI/CD pipelines, observability, incident response) for AI/ML workloads.
Experience in Life Sciences, Healthcare, or other regulated industries is strongly preferred.
Working knowledge of modern architectures (Lakehouse, vector search, semantic layers) and tools such as Databricks and Jira.

Scrum Master – Data & Analytics, AI/ML Ops

Key skills