Data Scientist
About the role
Property Finder is the leading property portal in the Middle East and North Africa (MENA) region, dedicated to shaping an inclusive future for real estate while spearheading the region's growing tech ecosystem. At its core is a clear and powerful purpose: To change living for good in the region.
Founded on the value of great ambitions, Property Finder connects millions of property seekers with thousands of real estate professionals every day. The platform offers a seamless and enriching experience, empowering both buyers and renters to make informed decisions. Since its inception in 2007, Property Finder has evolved into a trusted partner for developers, brokers, and home seekers. As a lighthouse tech company, it continues to create an environment where people can thrive and contribute meaningfully to the transformation of real estate in MENA.
Position Summary
We are looking for a Data Scientist with hands-on experience in machine learning and statistical methods to help build predictive and optimization models across our property portal. You will work on models that improve pricing accuracy, lead scoring, and listing quality, from feature development through deployment, working closely with engineering and business teams to deliver measurable impact.
Key Responsibilities
- Design and implement predictive and optimization models using classical ML, statistical modeling, and deep learning techniques.
- Build and deploy models on AWS (SageMaker, Lambda, ECS), owning the full path from feature extraction to production, including MLOps practices like CI/CD for models and monitoring.
- Build evaluation pipelines and support A/B testing frameworks to validate model performance and reliability.
- Collaborate with engineering teams on production deployment, ensuring models meet latency and scalability requirements.
- Contribute to model monitoring practices, tracking performance drift and data quality over time.
Cross-Team Collaboration
- Partner with engineering to optimize model deployment and…