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SeniorRemoteAustralia

Staff Data Scientist

J
jobgether
Зарплата
$18,800–$20,800
Уровень
Senior
Формат
Remote
О роли

Описание вакансии

About the company

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Scientist based in Australia.

Responsibilities
  • 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.
Conditions
  • Compensation range of $225,000–$250,000 per year.
  • Opportunity to work on high-impact statistical and machine learning systems used in residential real estate valuation.
  • Direct collaboration with senior technical leadership on challenging and open-ended analytical problems.
  • Hands-on ownership of production machine learning systems and next-generation valuation methodologies.
  • Opportunity to influence technical standards, engineering practices, hiring, and the long-term direction of a growing Data Science organization.
  • Significant potential for increased technical leadership responsibilities as the team expands.
  • Opportunity to work on interpretable and explainable machine learning systems where accuracy, transparency, and defensibility are critical.
Стек и навыки

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