Champion Scrum, Kanban, and SAFe principles while adapting them to suit product maturity and regulatory requirements.
Facilitate sprint planning, backlog refinement, daily stand-ups, sprint reviews, and retrospectives with technical and business stakeholders.
Drive incremental delivery and ensure teams deliver working, high-quality software on predictable timelines.
Remove delivery blockers proactively, from technical dependencies to cross-team alignment issues.
Technical Engagement
Understand system architecture, AI model lifecycle, MLOps pipelines, and data flows to better facilitate technical discussions.
Collaborate with Tech Leads, Data Scientists, ML Engineers, and DevOps teams to ensure sprint commitments are technically achievable.
Track code quality, automated test coverage, CI/CD health, and cloud infrastructure readiness as part of delivery metrics.
Ensure data privacy, security, and regulatory compliance are integrated into delivery workflows.
Stakeholder & Product Alignment
Partner with Product Owners, Clinical SMEs, Data Science, Data Engineering and Customer Success to ensure backlog priorities align with business outcomes and value driven
Ensure transparent and regular communication of progress, risks, and dependencies to leadership and stakeholders.
Metrics & Continuous Improvement
Establish and monitor KPIs such as sprint predictability, sprint metrics such as burn rate, lead time, defect leakage, and deployment frequency.
Drive retrospective outcomes into actionable improvements for team efficiency and product quality.
Introduce process automation, backlog grooming discipline, and release readiness checklists to optimize delivery.
Work Experience
Required Qualifications
8+ years in software delivery roles, with 5+ years as a Scrum Master or Agile Delivery Lead.
Proven track record in SaaS product delivery, preferably with AI/ML-powered platforms.
Strong technical foundation in cloud-native architectures (AWS/Azure/GCP), APIs, microservices, and data engineering workflows.
Excellent servant-leadership, facilitation, and conflict resolution skills
Proactive thinker with ability to influence stakeholder based on business objectives and value delivery
Strong analytical ability to interpret technical and business metrics for decision-making.
Familiarity with generative AI, NLP, and predictive analytics in healthcare contexts.
Familiarity with ML model development, MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI), and data governance
Experience in healthcare and/or pharma software, with strong understanding of EHR/EMR health records and HIPAA, GDPR, GxP, and FDA 21 CFR Part 11 compliance.
Understanding of clinical trial systems and/or RWE (Real World Evidence) platforms, or drug discovery pipelines.
