Posted:1 week ago|
Platform:
On-site
Full Time
Role Overview As a Scrum Master at Dailoqa, you’ll bridge the gap between agile practices and the unique demands of AI/ML-driven product development. You’ll coach cross-functional teams of software engineers, data scientists, and ML engineers to deliver high-impact solutions while fostering a culture of collaboration, experimentation, and continuous improvement. Key Responsibilities Agile Facilitation & Coaching Facilitate all Scrum ceremonies (sprint planning, daily stand-ups, reviews, retrospectives) with a focus on outcomes, not just outputs. Coach team members (software engineers, AI/ML engineers, data scientists) on Agile principles, ensuring adherence to Scrum frameworks while adapting practices to AI/ML workflows. Sprint & Workflow Management Manage hybrid sprints that include software development, data science research, and ML model training/deployment. Maintain Agile boards (Jira, Azure DevOps) to reflect real-time progress, ensuring transparency for stakeholders. Monitor sprint velocity, burndown charts, and cycle times, using metrics to identify bottlenecks and improve predictability. AI/ML-Specific Agile Leadership Adapt Agile practices to AI/ML challenges: Experimentation: Advocate for “spikes” to validate hypotheses or data pipelines. Uncertainty: Help teams embrace iterative learning and fail-fast approaches. Cross-functional collaboration: Resolve dependencies between data engineers, MLops, and product teams. Continuous Improvement Lead retrospectives focused on both technical (e.g., model accuracy, pipeline efficiency) and process improvements. Drive adoption of Agile engineering practices (CI/CD, test automation) tailored to AI/ML workflows. Qualifications Must-Have 5–8 years as a Scrum Master, with 2+ years supporting AI/ML or data science teams . Deep understanding of Agile frameworks (Scrum, Kanban) and tools (Jira, Azure DevOps). Proven ability to coach teams through AI/ML-specific challenges: Model lifecycle management (training, validation, deployment). Balancing research-heavy work with delivery timelines. Managing data dependencies and computational resource constraints. Certifications: CSM, PSM, or equivalent. • Strong understanding of Agile frameworks (Scrum, Kanban) and SDLC principles • Experience working with cross-functional teams including data scientists and AI engineers • Exposure to AI/ML product development cycles, including research-to-production workflows • Familiarity with AI project elements such as model training, data labeling, GenAI experimentation, or MLOps Show more Show less
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