Property Finder
Data Scientist (AI, Machine Learning, and Generative AI)
- Permanent
- Dubai, United Arab Emirates
- Experience 2 - 5 yrs
Job expiry date: 27/03/2026
Job overview
Date posted
10/02/2026
Location
Dubai, United Arab Emirates
Salary
Undisclosed
Compensation
Job description
Property Finder is the leading property portal in the Middle East and North Africa (MENA) region and a lighthouse technology company within the PropTech ecosystem, with a mission to change living for good in the region. Founded in 2007, the company connects millions of property seekers with thousands of real estate professionals, providing a seamless platform that empowers informed decisions for buyers and renters while supporting developers and brokers. The Data Scientist role is based in Dubai, United Arab Emirates, and sits within the AI & Data Science team, playing a critical role in shaping AI-driven decision-making and driving the company’s data strategy. The position requires a highly experienced Data Scientist with foundational expertise in Machine Learning Engineering and Generative AI, capable of designing and implementing advanced predictive and optimization models using classical machine learning, statistical models, deep learning approaches, and modern AI techniques. The role emphasizes applied innovation in Large Language Models, Generative AI, and Agentic AI systems, with responsibilities spanning experimentation, evaluation, and deployment of end-to-end machine learning models into production environments. The Data Scientist builds and maintains evaluation pipelines, A/B testing frameworks, and monitoring systems to ensure model performance, reliability, fairness, and explainability, while integrating trust, confidence scoring, and auditability into model design. The role involves developing advanced analytics and visualization frameworks, optimizing model scalability, efficiency, and real-world reliability, and implementing MLOps best practices including CI/CD, automated workflows, model registry, versioning, and lifecycle management. The position collaborates closely with Data Platform, Engineering, Strategy, Business Analytics, Commercial, and Product teams to align objectives, ensure production-grade scalability, low latency, and operational resilience, and deliver impactful AI solutions across enterprise platforms.
Required skills
Key responsibilities
- Design and implement advanced predictive and optimization models using classical machine learning, statistical models, deep learning, and modern AI techniques
- Drive applied innovation in Large Language Models, Generative AI, and Agentic AI systems to enhance user experience, automate workflows, improve lead qualification, personalized recommendations, and content creation
- Build end-to-end machine learning model development workflows including experimentation, rigorous evaluation, and deployment into production environments
- Develop and maintain robust evaluation pipelines, A/B testing frameworks, and model monitoring systems to ensure performance, reliability, fairness, and trustworthiness
- Develop and optimize advanced analytics and visualization frameworks to support large-scale data-driven decision-making
- Collaborate with engineering teams to ensure production-grade scalability, low latency, and operational resilience of deployed models
- Integrate trust, explainability, confidence scoring, fairness, and auditability into model design and deployment
- Continuously optimize machine learning models for scalability, efficiency, and real-world reliability
- Implement MLOps and deployment best practices including CI/CD pipelines, automated workflows, model registry, versioning, and lifecycle management
- Partner with Data Platform and Engineering teams to optimize model deployment and operational workflows
- Work closely with Strategy, Business Analytics, Commercial, and Product teams to align project objectives and ensure smooth deployment of AI solutions
Experience & skills
- Hold a Bachelor’s degree or higher in Computer Science, Data Science, Machine Learning, or a related field
- Have at least 3+ years of experience in Data Science or Machine Learning Engineering roles with foundational MLOps practices
- Demonstrate strong working knowledge of advanced predictive modeling, optimization techniques, scenario analysis, and statistical methodologies
- Exhibit proficiency in Python including pandas, NumPy, and Scikit-learn, with experience using PyTorch or TensorFlow for deep learning
- Demonstrate hands-on experience with supervised and unsupervised learning methods, evaluation metrics, feature engineering, and model tuning
- Possess working knowledge of deep learning techniques including CNNs, RNNs, and transformer architectures
- Show hands-on experience with Generative AI, Large Language Models, Agentic AI, prompt engineering, fine-tuning, and retrieval-augmented systems
- Have experience developing APIs and integrating AI systems with external platforms
- Demonstrate experience leveraging cloud platforms such as AWS, GCP, or Azure for scalable AI solutions
- Exhibit foundational understanding of machine learning pipelines, deployment processes, version control systems such as Git, CI/CD workflows, and containerization tools including Docker