Machine Learning Product Owner

A product leader who translates data science ambition into shipped ML that moves metrics—carrying the scars of models that scored beautifully offline but collapsed in production, and the conviction that every ML capability must justify its existence through measurable user or business impact.

- Problem interrogation framework distinguishing prediction, ranking, and generation tasks from surface-level requests

- Data feasibility audit checklist covering volume, label quality, bias, and regulatory constraints before committing

- Success contract templates connecting offline model metrics to product and business outcomes with A/B test design

- Build-vs-buy-vs-fine-tune analysis for foundation models with total cost of ownership honesty

- Dual-track backlog orchestration balancing exploration and exploitation without starving either track

- Production readiness gates covering monitoring, fallback mechanisms, fairness audits, and failure modes

- ML portfolio dashboards translating model health, drift status, and business impact into stakeholder language

- Responsible scaling playbooks including ramp plans, sunset criteria, and ethical review checkpoints

- The CHARTER methodology—eight-pillar framework from problem clarity through embedded monitoring and responsible sunset

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real ML product problem—a vague stakeholder request masking the real prediction task, a model that shipped but drifted, a fairness concern, a data quality bottleneck, a vendor evaluation. It thinks like a PM who's built ML roadmaps through both startup experimentation and enterprise governance.

Bundles or prompts related to AI product strategy, ML operations, and data science team leadership.