Data Scientist at Property Finder

Dubai, Dubai, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

23 Feb, 26

Salary

0.0

Posted On

25 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Generative AI, Predictive Modeling, Deep Learning, Python, API Development, Cloud Platforms, MLOps, Statistical Methodologies, Feature Engineering, Model Tuning, Collaboration, Communication, Strategic Thinking, Model Evaluation

Industry

Software Development

Description

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 highly experienced Data Scientist with foundational skills in Machine Learning Engineering, and Generative AI to join our innovative AI & Data Science team. The ideal candidate will bring expertise in advanced predictive modeling, scalable data solutions, and integrating AI systems into enterprise platforms. A strong research mindset is essential to excel in this role. This position plays a critical role in shaping the future of AI-driven decision-making at Property Finder, driving data strategy, and delivering impactful AI solutions. Key Responsibilities: Design and implement advanced predictive and optimization models, leveraging classical ML/Statistical model, deep learning models, and modern AI techniques. Drive applied innovation in Large Language Models (LLMs), Generative AI, and Agentic AI systems, exploring and developing novel applications to enhance and automate user experience, improve lead qualification, personalized recommendations, content creation, or automation and automate workflows. Build end-to-end model development, including experimentation, rigorous evaluation, and deployment into production environments. Build and maintain robust evaluation pipelines, A/B testing frameworks, and model monitoring systems to ensure performance, reliability, and fairness. Develop and optimize advanced analytics and visualization frameworks to support decision-making at scale. Collaborate with engineering teams to ensure production-grade scalability, low latency, and operational resilience of deployed models. Integrate trust and explainability into model design, generate confidence scores, ensure fairness, and maintain auditability. Continuously optimize model performance for scalability, efficiency, and real-world reliability. Implement MLOps and deployment best practices (CI/CD, automated workflows, model registry, versioning, and lifecycle management). Cross-Team Collaboration: Partner with the Data Platform and Engineering teams to optimize model deployment and operational workflows. Work closely with the Strategy, BA, Commercial, and Product teams to align on project objectives and ensure smooth deployment of solutions. Desired Qualifications: Education: Bachelor or above in Computer Science, Data Science, Machine Learning, or a related field. Experience: At least 3+ years in Data Science or MLE roles and foundational MLOps practices. Technical Skills: Working knowledge of advanced predictive modeling, optimization, scenario analysis, and statistical methodologies. Strong grasp of supervised/unsupervised methods, evaluation metrics, feature engineering, and model tuning. Proficiency in Python (pandas, NumPy, Scikit-learn); experience with PyTorch or TensorFlow for deep learning. Experience with API development and connecting AI systems to external platforms. Working knowledge in deep learning techniques, including CNNs, RNNs, and transformers. Hands-on experience in applied GenAI/LLMs/Agentic AI augmented with knowledge of transformer-based architectures, prompt engineering, fine-tuning, and retrieval-augmented systems. Experience in leveraging cloud platforms (AWS, GCP, Azure) for scalable AI solutions. Foundational understanding of ML pipelines and Hands-on experience in deployment processes, version control systems (e.g., Git), CI/CD workflows, and containerization tools like Docker. Soft Skills: Effective communication and collaboration skills, particularly in cross-functional environments. Strategic mindset with the ability to translate business goals into data-driven initiatives. Why Join Us? Contribute to cutting-edge AI and ML projects in a dynamic and fast-paced environment. Collaborate with top talent across diverse teams and disciplines. Be part of an innovative culture that values advanced research, strategic thinking, and impactful execution. Our promise to talent At Property Finder, we believe talent thrives in an environment where you can be your best self. Where you are empowered to create, elevate, grow, and care. Our team is made up of the best and brightest, united by a shared ambition to change living for good in the region. We attract top talent who want to make an impact. We firmly believe that when our people grow, we all succeed. Property Finder Guiding Principles Think Future First Data Beats Opinions, Speed Beats Perfection Optimise for Impact No Ostriches Allowed Our People, Our Power The Biggest Risk is Taking no Risk at All Find us at: Twitter Facebook Instagram Linkedin Glassdoor

Responsibilities

Design and implement advanced predictive and optimization models while driving applied innovation in AI systems. Collaborate with engineering teams to ensure the scalability and reliability of deployed models.