Data Scientist, Consumer Analytics

The Specialist II, Data Science applies advanced analytical methods and machine learning techniques to solve moderately complex business problems, deliver scalable data products, and generate insights that inform decision-making across Zillow. This role collaborates with cross-functional partners to translate ambiguous questions into structured analyses, build and maintain production-quality models, and continuously improve the quality, usability, and impact of Zillow’s data assets.

Responsibilities

With guidance from your manager, lead the design, development, and deployment of end-to-end analytical and machine learning solutions that address moderately complex business problems and customer use cases; Partner with product, engineering, and business stakeholders to frame ambiguous questions into testable hypotheses, define success metrics, and translate requirements into robust analytical or modeling approaches; Build, validate, and maintain scalable data pipelines, features, and models using modern data and ML tooling, ensuring reliability, reproducibility, and performance in production environments; Analyze large, complex datasets to uncover trends, drivers, and opportunities, and clearly communicate findings and recommendations to technical and non-technical audiences; Apply sound experimentation and evaluation practices (e.g., A/B testing, backtesting, error analysis) to measure impact, compare alternatives, and guide iteration on models and data products; Contribute to shared standards, documentation, and best practices for data quality, model governance, and responsible use of data and AI within the Data Science community at Zillow

Requirements

Bachelor’s degree in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline; 5+ years of experience applying data science, advanced analytics, or machine learning in a professional setting; Experience designing and implementing end-to-end analytical or ML solutions, including data preparation, feature engineering, model development, validation, and operationalization; Experience working with large datasets and modern data ecosystems (e.g., distributed data processing, cloud-based data platforms) to develop insights and data products that support business decisions; Experience collaborating with cross-functional partners to understand requirements, prioritize work, and deliver measurable outcomes using data and analytics; Or an equivalent combination of education and experience

Benefits

Equity awards