Description:

PriceHubble is a European B2B company that creates innovative digital solutions for the financial and real estate industries, focusing on property valuations and market insights.
The company utilizes big data, advanced analytics, and effective visualization to provide accurate data-driven insights for real estate and investment decisions.
The Property Intelligence Tribe is responsible for delivering insights that assist in making informed real estate decisions, including property value assessments and market trend tracking.
As a Senior Applied Data Scientist, you will work on machine learning systems that power the core services of the PriceHubble platform.
Your responsibilities will include designing, implementing, and maintaining Automated Valuation Models (AVMs) that are trusted by professionals for assessing residential property values.
You will develop product features that contribute to revenue growth and navigate cost-value trade-offs to deliver customer value.
The role involves leading projects from concept to launch, improving the performance and cost-efficiency of ML models, and maintaining ML systems.

Requirements:

A product-oriented mindset with enthusiasm for contributing to user-facing products, focusing on delivering customer value through machine learning.
Excellent English communication skills, both spoken and written, to effectively communicate with cross-functional partners and mentor fellow engineers.
Hands-on experience with a focus on writing clear, simple, and maintainable code, understanding that code is read more often than it is written.
A growth mindset, demonstrating a commitment to continuous learning and seeking new challenges.
Over 3 years of industry experience applying machine learning to solve business problems and writing production ML pipelines.
Bonus: Previous experience in PropTech and proficiency in working with geospatial data and leveraging geospatial features.

Competitive salary to attract top talent.
A Learning & Development program to support skill improvement and personal development.
Well-located offices with a flexible remote work policy, allowing work from various locations.
Flexible working hours to promote work-life balance.