Staff Data Scientist

Accountabilities Design, develop, and implement production-quality statistical and machine learning systems for residential property valuation and related analytical applications. Partner directly with technical leadership to develop innovative valuation methodologies and solve challenging statistical problems that may not have established solutions. Translate research concepts and analytical ideas into scalable, maintainable, production-ready software. Design rigorous evaluation frameworks to measure model performance, reliability, accuracy, and robustness, and continuously improve analytical outcomes. Develop methodologies for complex use cases, including rare and atypical properties, confidence estimation, and uncertainty quantification. Build interpretable machine learning systems that provide transparent and defensible results for consumers and professional users. Collaborate closely with software engineering teams to integrate new analytical capabilities into production systems. Improve the reliability, maintainability, testing, and overall engineering quality of the machine learning platform. Establish technical standards, modeling practices, and engineering best practices for the Data Science function. Contribute to technical hiring, mentoring, knowledge sharing, and the development of future Data Science team members. Help shape the long-term technical direction and capabilities of the growing Data Science organization. Requirements Strong professional background in machine learning and statistics, with experience applying advanced analytical techniques to complex business or technical problems. Demonstrated experience building and deploying production-quality machine learning systems rather than working exclusively in research or experimental environments. Excellent Python programming skills and strong software engineering fundamentals. Experience with testing, version control, modular architecture, maintainable code, and other practices required for reliable production software. Strong understanding of statistical modeling, interpretable machine learning, optimization, and rigorous model evaluation. Ability to reason from first principles and solve ambiguous, open-ended problems where established methodologies may not exist. Strong analytical and problem-solving skills, with an ability to develop elegant and defensible solutions to difficult statistical and engineering challenges. Excellent communication skills, including the ability to explain complex technical concepts clearly to both technical and non-technical stakeholders. Collaborative approach to solving challenging technical problems, with the ability to give and receive constructive technical feedback. Ability and interest in mentoring colleagues, contributing to technical standards, and helping shape a growing Data Science organization. A senior or staff-level mindset, with the technical depth and ownership required to influence modeling, engineering, and organizational direction.