Why This Job is Featured on The SaaS Jobs
This Service BI and Analytics Lead role stands out in the SaaS ecosystem because it sits at the intersection of product, support operations, and finance, translating post purchase signals into measurable service outcomes. While the remit includes hardware service, the operating model is recognisably SaaS: lifecycle analytics, telemetry informed decisioning, and tight feedback loops between customer experience and product quality. The emphasis on defining metrics and building a source of truth reflects a company still formalising how Service performance is understood.
For a SaaS career path, the long term value is in owning an end to end analytics layer that connects operational events to unit economics and planning. Work like forecasting demand, scenario modelling, and KPI governance builds durable skills for roles in Revenue Operations, Customer Experience analytics, and operational finance within subscription businesses. The cross functional scope also mirrors how modern SaaS organisations run operating reviews and prioritise investments with imperfect data.
This role tends to fit professionals who prefer ambiguity, enjoy building systems from first principles, and can move between executive narratives and hands on data work. It also suits leaders who like influencing across multiple functions without relying on direct authority, and who are motivated by creating decision frameworks that other teams will adopt.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Team
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.
About the Role
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.
This role is based in San Francisco, CA. We use a hybrid work model of 4 days in the office per week and offer relocation assistance to new employees.
In this role, you will:
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.
You might thrive in this role if you:
Have 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.
Have strong SQL, data modeling, and analytics fundamentals, with the ability to work across raw operational data and executive-level insights.
Have experience building analytics systems, KPI frameworks, and reporting from 0→1.
Have experience with forecasting, operational modeling, unit economics, and scenario planning.
Have strong judgment in ambiguous environments and can turn imperfect data into actionable direction.
Can influence cross-functionally across Operations, Product, Engineering, Finance, and Support.
Have experience leading, or are ready to lead, a lean team while setting a high bar for analytical rigor.
Have experience with warranty, claims, returns, repairs, RMA, or reverse-logistics analytics.
Have familiarity with product quality, reliability analytics, or cost-of-quality frameworks.
Have experience designing semantic layers, KPI governance, or source-of-truth metrics across multiple teams.
Have worked in global support, multi-market service environments, AI-enabled support operations, agent tooling, or telemetry-driven service models.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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