• Support the development of statistical and machine learning models, both supervised and unsupervised. • Prepare, clean, and transform datasets (ETL/ELT processes). • Perform exploratory data analysis (EDA) to identify patterns, trends, and opportunities for improvement. • Create dashboards and automated reports using Power BI, Python, or similar tools. • Participate in the development and maintenance of data pipelines in collaborative environments. • Document processes, code, analyses, and results to ensure traceability and knowledge sharing. • Collaborate with business teams to understand problems and translate needs into analytical solutions. • Contribute analyses and studies that support the company’s strategic decisions.

• Building strong relationships with cross-functional partners across Product, Design, Engineering, and Analytics to drive collaboration and innovation. • Contributing directly to launching new data-driven products to help the Airbnb guest and host community. • Writing software in Python, SQL, or R to model, simulate, and measure the impact of new product features for both guests and hosts. • Analyzing structured or unstructured data to uncover meaningful insights and craft actionable proposals. • Presenting findings and recommendations to leaders and stakeholders in a clear, compelling manner that drives informed data-driven decision making.

• Contribute to the development of a pricing guidance system for hosts. • Drive innovative pricing science through strategic initiatives, including early-stage and ambiguous 0-to-1 projects. • Collaborate with product and cross-functional teams to pioneer pricing strategies and guidance for hosts. • Develop foundational models and experimental approaches that balance supply and demand in the marketplace. • Craft compelling data narratives to surface actionable insights.

• Elicit technical requirements and propose efficient data-oriented solutions. • Define and implement data models to meet business requirements. • Develop Data Science solutions involving supervised and unsupervised models, predictive modeling, and exploratory analyses. • Execute all stages of the Data Science solution lifecycle: business understanding, feature/variable preparation, model development, and performance validation. • Assist in building and maintaining data engineering pipelines, ensuring scalability and security. • Design and implement model usage and monitoring practices together with business areas. • Implement systems and routines that ensure data quality and consistency. • Maintain technical documentation for implemented solutions. • Communicate complex insights simply and effectively to non-technical stakeholders. • Interact with professionals across different profiles and seniority levels in a dynamic, changing environment.