Service BI / Analytics Lead

OpenAI•San Francisco, CA

1d•Hybrid

About The Position

The Consumer Device Service team is responsible for building the post-purchase experience for a new category of AI-powered hardware products. The team works across Support, Warranty, Returns, Reverse Logistics, Product Quality, Commerce, Finance, and Operations to ensure customers have a seamless ownership experience after purchase. As a Service BI / Analytics Lead, you will define how Service measures, understands, and improves the customer experience across the full hardware service lifecycle, from onboarding and troubleshooting to warranty claims, replacements, repairs, reverse logistics, and loyalty outcomes. You will build the analytics foundation and insight engine that powers decision-making across Service. This includes defining metrics, building reporting systems, forecasting warranty and service demand, modeling unit economics, and surfacing insights that help improve both customer experience and operational discipline. We’re looking for people who can operate at both strategy and execution depth, are comfortable building analytics systems from first principles, and can turn imperfect data into clear recommendations in a 0→1 environment.

Requirements

8–12+ years of experience in BI, analytics, data science, or a related function, ideally in consumer hardware, customer support, warranty, operations, or reverse logistics.
Strong SQL, data modeling, and analytics fundamentals, with the ability to work across raw operational data and executive-level insights.
Experience building analytics systems, KPI frameworks, and reporting from 0→1.
Experience with forecasting, operational modeling, unit economics, and scenario planning.
Strong judgment in ambiguous environments and can turn imperfect data into actionable direction.
Can influence cross-functionally across Operations, Product, Engineering, Finance, and Support.
Experience leading, or are ready to lead, a lean team while setting a high bar for analytical rigor.
Experience with warranty, claims, returns, repairs, RMA, or reverse-logistics analytics.
Familiarity with product quality, reliability analytics, or cost-of-quality frameworks.
Experience designing semantic layers, KPI governance, or source-of-truth metrics across multiple teams.
Worked in global support, multi-market service environments, AI-enabled support operations, agent tooling, or telemetry-driven service models.

Responsibilities

Define the Service analytics strategy, including core metrics, reporting logic, and decision frameworks across customer experience, operational performance, warranty, and cost.
Create a unified analytics model across Support, Warranty, Returns, Reverse Logistics, Product Quality, Commerce, and Finance.
Partner with Data Engineering and systems teams to build the data models, pipelines, joins, and semantic layers required to support hardware service analytics.
Build reporting capabilities for returns and warranty, including replacement, repair, RMA, depot, reverse-logistics, and finance-reconciliation flows.
Establish source-of-truth data across accounts, devices, orders, claims, logistics events, diagnostics, and support interactions.
Lead forecasting for total warranty return rate, warranty cost, and related operational demand using quality signals, telemetry, claims history, install base, policy assumptions, and launch cohorts.
Define and measure service unit economics, including cost per shipped unit, cost per active unit, cost per contact, cost per claim, and cost per resolved case.
Build scenario models that inform replacement planning, repair demand, return exposure, inventory needs, staffing, and broader service investment decisions.
Lead deep-dive analyses on cost of quality, claim drivers, repair vs. replacement tradeoffs, churn and loyalty drivers, and opportunities for differentiated care programs.
Identify root causes of returns, replacements, repairs, and negative service outcomes by connecting support, product-quality, telemetry, and logistics data.
Partner with the broader hardware operations team on analytics related to supply and demand planning, parts availability, replacement inventory risk, and launch readiness.
Work cross-functionally with Product, Engineering, Product Quality, Reverse Logistics, Commerce, Finance, and Support Operations to turn data into clear recommendations and decisions.
Build executive dashboards and operating-review materials that make service performance, risk, and opportunity easy to understand.
Shape the roadmap for analytics investments across systems, reporting, forecasting, and insight generation.
Over time, hire and lead a small, high-impact analytics team as the hardware service organization scales.