Scrum Master(AI & Data Science), AI/ML Scrum Master

Role name

Years of exp

Location

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