Data Scientist

The Main Event: What You’ll Drive, Build, and Own¿Todo listo para enviar su solicitud?Asegúrese de comprender todas las responsabilidades y tareas asociadas a este puesto antes de continuar.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 HuspyProven 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.xcskxljAcademic Background: Bachelor’s in STEM (Master’s a plus).#J-*****-Ljbffr

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. xcskxlj Academic Background: Bachelor’s in STEM (Master’s a plus). #J-*****-Ljbffr