Scrum Master (AI/ML Initiatives)

We are looking for a Scrum Master who understands that AI development isn't a linear path. You will be the "buffer and the bridge" for our AI Research and Engineering teams. Your goal is to apply Agile principles to a world of experimental data, model training, and compute constraints, ensuring we deliver high-value intelligence to our users at a steady cadence.

Key Responsibilities

• Agile Orchestration for AI: Facilitate all Scrum ceremonies (Daily Stand-ups, Sprint Planning, Reviews, Retrospectives) specifically tailored for the CRISP-DM or Agile ML lifecycle.

• Managing Uncertainty: Help the team navigate the "Research" phase of AI where outcomes are non-deterministic. You’ll assist in breaking down "Experimental" tasks into actionable user stories.

• Eliminating Friction: Proactively identify and remove blockers, such as data access delays, GPU/Compute bottlenecks, and shifting labeling requirements.

• Stakeholder Education: Manage expectations with product owners and executives who may be used to traditional software timelines, explaining the iterative nature of model performance and "Gold Standard" datasets.

• Metrics that Matter: Track traditional velocity alongside AI-specific health metrics like Model Decay, Training Cycles, and Inference Latency impacts on the backlog.

Required Qualifications

• Agile Certification: CSM (Certified Scrum Master), PSM, or equivalent.

• AI/ML Literacy: While you don't need to write code, you must understand the vocabulary. You should know the difference between training, validation, and deployment, and understand what a "Feature Store" or "Vector DB" is in a workflow.

• Tooling Expertise: Mastery of Jira or Azure DevOps, specifically configured for iterative R&D workflows (e.g., using "Spikes" for data exploration).

• Conflict Resolution: Proven ability to coach highly technical Data Scientists and Machine Learning Engineers on the value of "Done" in an experimental environment.

Preferred Skills

• Experience with MLOps workflows and how they integrate into CI/CD pipelines.

• Knowledge of the Azure AI ecosystem or AWS SageMaker project management.

• Background in Manufacturing, Finance, or Healthcare (where AI compliance and accuracy are critical).