Data Scientist in London

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The Story So FarHuspy is one of the leading property technology companies in EMEA. Launched in 2020, we now operate in multiple cities across the UAE and Spain, expanding into Saudi Arabia and 3 more European markets by 2026. 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 home‐buying 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.

The Perfect Match: What It Takes to Succeed at Huspy

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).

Seniority LevelMid‐Senior level

Employment TypeFull‐time

Job FunctionEngineering and Information Technology

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