Netomi - Lead / Senior Product Manager Analytics
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Requirements
• 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. • Preferred Qualification • 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).
Responsibilities
• 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.
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