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Associate Data Scientist (f/m/d)

Predium · Munich

Publicada em 26/09/2026

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Our OpportunityOver the past three years, Predium has built the leading real estate intelligence platform for ESG and value management in the DACH region - with the strongest names in the industry as customers: asset managers, housing companies, banks. Our customer base, our data, our credibility - that's valuable capital for the next step. Investment, credit, and asset management decisions in real estate are still made in Excel, case by case. We're now building the AI analyst that takes this work off people's hands - working directly on the customer's own data, scalable without adding headcount. We're well-funded, and we're bringing AI into real estate.Your MissionAs a Data Scientist at Predium, you will help develop and improve the data-driven models behind our platform. You will use statistics, machine learning, and numerical methods to solve well-defined problems related to buildings, energy performance, and real-estate decarbonization.Working closely with our engineering and product team, you will turn real-world data into reliable analytical, simulation, and machine-learning models that identify optimization opportunities for more sustainable buildings and help customers make informed investment decisions.What you'll ownDevelop and improve analytical, machine-learning, and simulation models to address business problems in the real-estate and energy domain.Build and improve data extraction pipelines for structured and unstructured sources, evaluating extraction accuracy and reliabilityContinuously explore and evaluate new data sources to improve model accuracy.Assess data quality, identify gaps and evaluate model performance by validating assumptions and results against real-world data and feedback of domain experts.Collaborate with data scientists, engineers, product managers, and other stakeholders to translate business needs into data-driven solutions.What success looks likeYou take ownership of well-defined analytical and modeling problems and independently
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