Staff Data Scientist

Jobgether; España; Presencial; EUR 195.000 - 216.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Salary $225,000-$250,000

Descripción de la vacante

Jobgether, on behalf of a partner company, is seeking a Staff Data Scientist based in Spain to shape valuation models for residential real estate and to build production-grade analytical systems. You will work with executive leadership to solve complex statistical problems, translate research into scalable software, and develop interpretable ML solutions used by consumers and professionals.

This senior role offers influence on standards, mentorship, and the long-term direction of the Data

Formación

• Proven experience deploying production ML systems.
• Strong Python programming and software fundamentals.
• Excellent communication with technical and non-technical stakeholders.

Responsabilidades

• Design and deploy production-grade ML systems for residential property valuation.
• Collaborate with leadership to develop valuation methodologies and solve challenging problems.
• Translate research into scalable, production-ready software.
• Build evaluation frameworks for model performance and robustness.
• Mentor team members and help set DS standards.

Conocimientos

Machine learning

Statistics

Python

Production systems

Software engineering

Communication

Herramientas

Git

CI/CD

Docker

Descripción del empleo

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 Spain.

This is a high-impact opportunity for a senior data science professional to help shape the future of statistical modeling and machine learning in residential real estate valuation.

You will work closely with executive technical leadership to solve complex, open-ended statistical and engineering problems.

The role combines hands-on development with the creation of new valuation methodologies and production-grade analytical systems.

Your work will directly support products and platforms used by consumers, financial stakeholders, and professional appraisers across the United States.

The focus is on interpretable machine learning, statistical modeling, optimization, uncertainty estimation, and reliable production systems rather than deep learning or LLM research.

As the Data Science organization grows, you will also help establish technical standards, mentor future team members, and influence its long-term direction.

This role is ideal for a technically strong, curious problem solver who enjoys turning novel analytical ideas into trusted software.

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.

Benefits

• 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.