Lead / Senior Product Manager Analytics

Define and execute the Analytics/Evals/Governance roadmap with clear sequencing (MVP → v1 → v2) and measurable adoption targets.

Partner deeply with Data Science to productize evaluation methodology (what we score, how we calibrate, how we prevent “gaming,” how we track drift).

Partner with Engineering and Observability teams to standardize telemetry (tool spans, workflow transitions, model calls, cost/latency metrics) and make it usable in product.

Drive a cohesive Agentic Studio UX across Build / Operate / Improve workflows: dashboards, drill-downs, investigation flows, alerts, and remediation actions.

Establish objective success metrics and instrument them end-to-end (data correctness, timeliness, reliability, and customer impact).

Work with Delivery/CS and enterprise partners to ensure analytics is usable for real operational processes: incident response, change management, governance reviews, and quarterly business reviews.

Bachelor’s degree in Computer Science, Data Science, Engineering, Statistics, or a related quantitative/technical field (Master’s a plus).

6–10+ years of experience across Product Management and/or Data Science/Analytics leadership, including shipping analytics products to enterprise users.

Demonstrated depth in: data modeling, telemetry/event schemas, ETL/ELT concepts, and analytics correctness.

Building operational + business reporting (real-time monitoring and historical analytics).

Experimentation and measurement design (A/B testing concepts, uplift measurement, cohorting).

Strong hands-on experience applied to AI/LLM or data science platforms, including at least several of:

LLM evaluation methods (automated scoring, multi-turn evaluation, calibration).

Observability for probabilistic systems (quality + latency + cost).

Tool calling / workflow execution analytics and failure taxonomy.

Governance measurement (policy outcomes, escalation triggers, compliance reporting).

Strong product craft: UX and information architecture instincts, ability to design operator workflows, and capability packaging that scales across customers.

Experience building analytics for multi-tenant SaaS and regulated customers (auditability, RBAC, retention controls, privacy-by-design).

Familiarity with cloud analytics stacks and enterprise BI integration patterns.

Experience partnering with major cloud and frontier AI vendors (model providers, observability stacks, data platforms) to ship joint capabilities.

Experience with contact center / CX metrics (containment, resolution, AHT, CSAT, handoff performance) and multi-channel operations (voice + digital).