SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
OpenAI is hiring a Data Scientist to support the Real Estate & Workplace (REW) team, a global group focused on creating workplaces that enable OpenAI's people to do their best work while scaling the company's real estate and operations. This is a high-ownership role spanning analytical strategy and hands-on execution at the intersection of workplace operations and AI-native analytics.
You will own ambiguous, high-impact problems end-to-end—from framing and prioritization through delivery, adoption, and iteration. Your responsibilities include defining success metrics and building measurement, forecasting, experimentation, and optimization frameworks for decisions about people, spaces, and investments. You'll develop and own agentic dashboards, reporting, models, and decision tools, ensuring they are trusted, governed, and agent-ready.
The role requires connecting data from workplace sensors, collaboration tools, operations, and financial systems while identifying and integrating novel data sources to guide decisions about space, services, employee experience, and investments. You'll communicate assumptions, trade-offs, and recommendations clearly to technical, operational, and executive stakeholders, working closely with Finance, People Analytics, IT, and REW leadership.
REW's scope spans portfolio strategy, design and construction, space planning, sustainability, workplace experience, and global operations. You'll work across a 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.
You should have experience in Data Science or Decision Science, ideally in operational, financial, workplace, or internal-tools environments. Strong grounding in applied statistics, causal inference, forecasting, and model evaluation is essential, along with fluency in SQL and Python. You must thrive in ambiguity, take ownership, and balance rigor with speed. Nice-to-have skills include familiarity with AI-assisted analytics, collaboration technologies, operational platforms, and experience building operational automation or decision systems.
