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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Huspy - **Location:** Illes Balears, Spain - **Contract:** Permanent contract - **Skills:** Data Analysis, Continuous Integration, Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Management of Software Versions, Core Voice Platform, Pandas, Scikit Learn, Machine Learning Operations, Natural Language Understanding, Data Pipelines - **Published:** July 27, 2026 - **Apply:** https://www.buscojobs.com.es/data-scientist-en-islas-baleares-ID-364722270 ## About the Role 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/Num Py/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). #J-*****-Ljbffr ## Description 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/Num Py/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).#J-*****-Ljbffr ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Machine learning 101: Where to begin?](https://www.wearedevelopers.com/videos/1014-machine-learning-101-where-to-begin) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [13 AI Tools You Have to Try](https://www.wearedevelopers.com/magazine/219-13-ai-tools-you-have-to-try) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)