Lead Product Manager developing AgeKeys, a privacy - preserving age credential system for age verification. Collaborating with partners and leading product strategy in a global context.
About the role
- Product Manager overseeing customer-facing dashboards and metrics evaluation for an AI-powered SaaS startup. Collaborating with engineering for data-driven product decisions and user experience enhancements.
Responsibilities
- Own Metrics & Evaluation Frameworks: Define and evolve the core Metrics framework for AI readiness, safety, security, and compliance.
- Partner with ML/eng to design metrics and evaluation loops that reflect real-world risk.
- Work with GTM and customers to ensure metrics map cleanly to business outcomes and internal reporting needs.
- Build Customer Dashboards & UX: Own the end-to-end UX for customer-facing dashboards: readiness scores, risk heatmaps, experiment views, trend charts, and drill-downs into specific conversations.
- Translate complex technical signals (attack success rates, severities, false positives/negatives) into simple visualizations and narratives.
- Work closely with design to define information architecture, layouts, and interaction patterns.
- Drive Customer Discovery & Feedback Loops: Run customer discovery with T&S leaders, PMs, compliance, and security stakeholders to understand how they make decisions and what data they trust.
- Turn feedback and usage data into a prioritized roadmap for dashboards, reports, and self-serve analytics.
- Experimentation & Data-Driven Decisions: Define success metrics and guardrail metrics for new features; partner with eng/ML on rollouts.
- Use product analytics and customer data to understand adoption, engagement, and impact of dashboards and reports.
Requirements
- 4–8+ years of Product Management experience in B2B SaaS and/or AI/ML-powered products.
- Demonstrated experience owning customer-facing dashboards, analytics products, or reporting surfaces.
- High comfort with metrics: defining KPIs, reasoning about tradeoffs, and turning raw data into clear definitions and thresholds.
- Strong collaboration with engineering, ops, and data/ML teams
- Excellent written and verbal communication; you can explain complex AI risk concepts in simple language to non-technical stakeholders.
- Experience with LLMs, GenAI, or ML-based products
- Background working with trust & safety, security, compliance, or risk teams.
- Prior startup experience building v1 products and iterating quickly with design partners.
Benefits
- Offers Equity
- Offers Bonus
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