Data Scientist
Indexed description
Today, we own the largest portion of the UAE mortgage market and are one of the fastest-growing players in every European city we’ve entered.We’ve raised over $140 million (Series A and Series B) from the world’s top investors, including Sequoia Capital, Founders Fund and Balderton Capital, to reshape the homebuying journey through powerful technology and agent-first tools.
We’ve built a SuperApp that empowers real estate agents and mortgage brokers, bringing cutting‑edge technology to one of the world’s most traditional industries. We’re transforming how property transactions happen — faster, smarter, and better for everyone.We’re not slowing down.
The question is: will you be part of what’s next?
The Main Event: What You’ll Drive, Build, and Own
- Real Estate Market Modeling: Build models applied to challenges such as valuation/pricing leveraging techniques from classic supervised ML to more advanced approaches.
- Multimodal Embeddings: Create vector representations of Real Estate entities, such as listings, combining images, text, and structured attributes to power search, matching, deduping, or recommendations.
- Data Analysis & Experimentation: Use SQL/Python to extract, clean, and analyze data; design experiments and evaluate model-product impact with robust metrics.
- Model Operationalization: Ship models to production with capabilities such as monitoring, automated rollout, or CI/CD (in partnership with engineering).
- Cross-functional Delivery: Partner with product, engineering, and operations teams to translate business problems into scalable ML solutions.
- Proven Experience: 4–8 years in applied data science/ML, delivering models that move real-world KPIs.
- SQL & Python Mastery: Strong in frameworks such as Pandas/NumPy/Scikit-learn...building reliable data pipelines, model training and evaluation.
- MLOps Fundamentals: Experience deploying/maintaining models (batch or real-time), versioning, CI/CD basics, observability, and reproducible training.
- Communication & Ownership: Clear with technical/non-technical stakeholders; can scope, prioritize, and explain tradeoffs.
- Comfortable with uncertainty, data quality issues, leakage risks, and market dynamics (location, seasonality, inventory shifts).
- Nice to Have: Software engineering experience; multimodal/vision experience; voice AI (ASR/NLU) exposure.
- Academic Background: Bachelor’s in STEM (Master’s a plus).
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