Product Manager
Listed on 2026-01-01
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IT/Tech
AI Engineer, Data Analyst
About Us
We’re an early-stage B2B SaaS company building AI-powered products for enterprise teams. Our customers care deeply about safety, trust, compliance, and revenue impact, and we help them launch AI features confidently.
We’re looking for a Product Manager who can own customer-facing dashboards end-to-end, defining the right KPIs, partnering with eng/ML on the data and evaluation, and turning complex signals into simple, decision-ready UI for our customers.
What You’ll DoOwn 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.
Must-Haves
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.
Nice‑to‑Haves
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.
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