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.

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