Data Scientist (m/f/d)

Shape the Future of Data-Driven Decision Making as our Data Scientist (m/f/d) At Limehome, data is one of our biggest competitive advantages. As a Data Scientist, you'll turn complex business challenges into scalable statistical and machine learning solutions that help us make smarter decisions across pricing, operations, product, and beyond. Working closely with Revenue Management, Product, Engineering, Business Intelligence, and other stakeholders, you'll combine advanced statistical thinking with commercial acumen to create measurable business impact while continuously raising the bar for data-driven decision-making. How you’ll be hosting with heart: Design, develop, and continuously improve our core pricing algorithms—our highest-impact analytical products—to maximize revenue, occupancy, and long-term profitability. Design, develop, and evaluate advanced statistical models and machine learning algorithms to solve complex business challenges and enable smarter decision-making. Apply rigorous statistical methodologies to quantify uncertainty, estimate business impact, and generate reliable recommendations for decision-making. Design experiments and define evaluation frameworks to measure the effectiveness of new products, pricing strategies, and business initiatives. Analyze large and complex datasets to uncover opportunities for growth, operational improvements, and enhanced customer experience. Partner with Revenue Management, Product, Engineering, and BI to translate business questions into scalable analytical solutions and collaborate with Data Engineering to productionize, deploy, and monitor machine learning models. Stay at the forefront of AI, ML, and decision science to elevate our analytical capabilities, while clearly communicating complex findings to both technical and non-technical audiences. What you need to shoot for the stars: Degree in Data Science, Statistics, Mathematics, Computer Science, or a quantitative field combined with 3+ years of hands-on experience applying machine learning/statistics to real-world business problems. Strong foundation in probability theory, statistical inference, regression analysis, Bayesian statistics, experimentation, causal inference, and predictive modelling. Proven experience developing, validating, and improving machine learning models in business environments. Strong programming and modelling skills in Python and experience with modern data science libraries. Excellent SQL skills and experience working with large analytical datasets. Experience designing experiments and evaluating business initiatives, balancing statistical rigor with pragmatic decision-making to deliver simple, high-value solutions over unnecessary complexity. Strong commercial mindset with a passion for exploring new analytical methods/AI tech and translating complex findings into measurable business recommendations. Fluency in English with excellent communication and stakeholder management skills alongside the ability to e