Data Scientist

Opendoor • Toronto, ON

171d • $169,600 - $233,200 • Onsite

About The Position

Opendoor is seeking an experienced Data Scientist to address important, ambiguous problems across marketing, pricing, and product. The role applies statistical reasoning, experimentation, causal inference, and business judgment to shape strategy and produce measurable impact. Work may include defining metrics, designing experiments, evaluating opportunities, and guiding high-stakes business decisions.

The Data Scientist will be anchored to a team and problem area based on business needs and expertise. Across domains, the role partners closely with cross-functional teams, defines success, guides roadmaps, and selects analytical approaches suited to the decision at hand. The position is based in one of Opendoor’s talent hubs and requires working in the office five days a week, with Monday, Tuesday, Thursday, and Friday specifically required.

Requirements

• Deep statistical reasoning, including experimental design, hypothesis testing, causal inference, and distinguishing meaningful signal from noise.
• Strong SQL and Python skills, with experience working with large, complex, real-world datasets.
• Familiarity with production data pipelines, distributed computing, and modern analytics tooling.
• Deep expertise in one or more areas such as experimentation, causal inference, statistical analysis, forecasting, or optimization.
• Ability to structure open-ended problems, make sound methodological tradeoffs, and maintain a high bar for data quality and validation.
• A track record of translating rigorous analysis into clear recommendations and measurable business or customer impact.
• Strong communication and collaboration skills, including the ability to influence partners across functions and levels of technical depth.
• Experience as a data scientist, product analyst, applied scientist, economist, or in a similar quantitative role.

Responsibilities

• Frame ambiguous business questions as clear analytical problems with measurable outcomes.
• Design metrics, dashboards, and decision frameworks to help teams understand performance, evaluate tradeoffs, and identify opportunities.
• Design and analyze experiments; apply causal inference and other rigorous methods when randomized tests are not practical.
• Use statistical analysis, forecasting, and other quantitative methods to evaluate opportunities and inform decisions.
• Own work end to end, from data discovery and validation through analysis, recommendation, and impact measurement.
• Partner with Product, Engineering, Marketing, Operations, and business leaders to shape roadmaps and translate insights into action.
• Communicate findings, uncertainty, tradeoffs, and recommendations clearly to technical and non-technical audiences, including senior leaders.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed