Data Scientist, Real Estate & Workplace

About the Role We’re hiring a Data Scientist to support Real Estate & Workplace (REW), a fast-moving global team focused on creating workplaces that help OpenAI’s people do their best work while scaling the company’s real estate and workplace operations. Our work is grounded in understanding how people use space and services, collaborate across physical and digital environments, and experience the workplace. REW’s scope spans portfolio strategy, design and construction, space planning, sustainability, workplace experience, and global operations. You’ll work comfortably across this broad, sometimes messy data landscape and build trusted relationships across the domain. The work informs high-impact decisions with immediate, visible effects—from where teams work and how space and services are allocated to which investments move forward and how workplace experiences evolve. This is a high-ownership Data Science role spanning analytical strategy and hands-on execution. Working at the forefront of AI-native analytics, you’ll help define the future of workplace operations at OpenAI rather than follow an established playbook. You’ll shape REW’s Data Science roadmap, identify where forecasting, experimentation, and optimization can drive impact, and translate business priorities into an analytical plan. You’ll own the stakeholder-facing execution layer—including owning agent-built dashboards, recurring reporting, models, and decision tools—along with analytical requirements, validation, adoption, and measurable business impact. In this role, you’ll be partnered closely with Finance, People Analytics, IT, and REW leaders. You’ll own problems end to end—from framing and prioritization through analysis, recommendation, delivery, adoption, and iteration—so the work drives measurable business outcomes. What You’ll Do - Own ambiguous, high-impact problems end to end—from framing and prioritization through delivery, adoption, and iteration. - Define success metrics and build measur

Department: Data Science; Team: Data Science