Lead Data Scientist

At Compass, our mission is to help everyone find their place in the world. Founded in 2012, we’re revolutionizing the real estate industry with our end-to-end platform that empowers residential real estate agents to deliver exceptional service to seller and buyer clients. About the Role: We are seeking a highly skilled and motivated Lead Data Scientist to join our data science team. In this role, you will leverage your deep expertise in machine learning, statistical modeling, and data analysis to solve our most complex problems. You will set the standard for data science excellence, architect scalable solutions, and help define the long-term analytical strategy. You will partner with senior business stakeholders and engineering leaders to uncover insights, develop cutting-edge data-driven solutions, and drive initiatives that directly shape company-wide strategic planning, resource allocation, and product innovation. Responsibilities: Lead the end-to-end development, validation, and deployment of large-scale predictive models and algorithms that inform strategic business decisions and market trend analysis. Design and execute rigorous data-driven research to analyze the impact of multi-faceted factors on business outcomes, tackling the organization’s most highly ambiguous and open-ended problems. Collaborate deeply with senior business stakeholders and engineering partner [... source excerpt omitted ...] audiences. Act as a technical mentor to other data scientists, fostering a culture of continuous learning, rigorous peer review, and adherence to state-of-the-art methodologies. Qualifications: Master's degree or PhD in Computer Science, Statistics, Economics, Mathematics, or a related quantitative field. 5+ years of experience in data science with a proven track record of conceptualizing, leading, and delivering highly successful, end-to-end data science projects. Deep expertise in key data science domains (e.g., time-series forecasting, deep learning, causal inference). Extensive practical experience architecting solutions using a broad range of methodologies (e.g., prediction,