Data Scientist, Analytics Enablement

The Strategic Finance Data Science team helps OpenAI understand business performance and make informed decisions about growth, revenue, and resource allocation. We partner with Strategic Finance, Data Engineering, and business teams to build trusted metrics, explain what is changing, and support forecasting, planning, pricing, and investment decisions. We're looking for a Data Scientist, Analytics Enablement to turn complex financial and business questions into reliable data products, clear analyses, and actionable recommendations. This is a hands-on individual contributor role. You will combine the technical depth of analytics engineering with strong analytical judgment and close partnership with Strategic Finance.

Responsibilities

Define and maintain trusted metrics and analytical datasets for revenue, usage, customer performance, compute consumption, and margin, with clear definitions and appropriate product, customer, and cohort detail; Build automated dashboards and recurring reporting that help Finance leaders and operating teams understand performance, spot changes, and prioritize investigation; Explain movements in business results and variances against expectations by examining underlying drivers, validating data quality, and distinguishing evidence from assumptions; Translate ambiguous questions about growth, retention, monetization, pricing, and resource efficiency into structured analyses and practical recommendations; Partner with forecasting specialists and Finance to provide reliable inputs, driver analyses, and scenario assumptions for forecasts and long-range plans; Work with Data Engineering to improve the quality and usability of underlying data, and develop reusable analytical models and workflows that reduce manual effort; Communicate findings clearly, align stakeholders on trade-offs, and follow through to understand whether the analysis or data product improved the decision

Requirements

Have strong SQL and analytical data-modeling skills, and experience turning large, complex datasets into reliable analyses and reusable data products; Can reason carefully about metrics, cohorts, denominators, measurement changes, and uncertainty, and investigate whether an unexpected result reflects the business or the data; Have built dashboards or recurring reporting used by business partners, and can explain the decisions they enabled; Bring sound business judgment and can independently scope an ambiguous problem, prioritize the work, and deliver an end-to-end solution; Communicate technical findings clearly and work effectively with Finance, data scientists, engineers, and other business partners; Can use Python or a comparable analytical tool to investigate data and automate repetitive work