Data Scientist

AI in this role

Design, build, and deploy machine learning and predictive models to improve pricing accuracy and lead scoring on a property portal.

pytorchtensorflowscikit-learnsagemakerpythonsqlawspandasnumpy

prompt-engineeringml-opscomputer-visionnlpmachine-learningstatistical-modelingdeep-learningmlopsa-b-testing

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 operational workflows.
Work with Product, Strategy, and Commercial teams to align on project objectives and translate business goals into data-driven initiatives.

Desired Qualifications

Education

Bachelor's or above in Mathematics, Computer Science, Data Science, or a related quantitative field.

Experience

4+ years in Data Science or Analytics roles with a demonstrable track record of business impact.

Technical Skills

Strong SQL and Python (pandas, NumPy, Scikit-learn), with experience in PyTorch or TensorFlow.
Hands on experience with supervised and unsupervised methods, evaluation metrics, feature engineering, and model tuning.
Experience deploying models on AWS, specifically SageMaker, Lambda, and ECS.
Solid understanding of statistical methods, experimental design, and hypothesis testing.

Nice to Have

Experience with model explainability (e.g. SHAP), as our models influence pricing and search outcomes.
GenAI experience and prompt engineering.
Computer vision or NLP background.

Soft Skills

Strong communication skills, able to translate complex findings for non-technical audiences.
Strong problem-solving ability, translating ambiguous business problems into technical solutions.

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
Act Like An Owner
The Biggest Risk is Taking no Risk at All

Find us at:

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How we rate this

Data Scientist at Property Finder rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Data Scientist is an open job at Property Finder.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Cairo, Egypt.
The job was posted on September 24, 2026.
Seniority: senior.
Applications are handled by Property Finder. Apply on Property Finder

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringML OpsComputer VisionNLPMachine LearningStatistical ModelingDeep LearningMlops

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
Tell me about a project where machine learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Prompt Engineering, ML Ops, Computer Vision, NLP, and Machine Learning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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