Senior Data Scientist

Zillow

6d•$141,200 - $237,400•Remote

About The Position

The Senior Specialist, Data Science applies advanced analytical methods and machine learning techniques to solve complex business problems, generate actionable insights, and improve Zillow’s products and customer experiences. The role independently drives end‑to‑end data science projects, partnering with cross‑functional teams to define problems, build and evaluate models, and translate findings into scalable, data‑informed decisions. This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions.

Requirements

Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative field.
8+ years of professional experience applying statistical analysis, machine learning, or advanced analytics to real‑world business problems in a data‑driven environment.
Demonstrated experience working with large, complex datasets using programming languages such as Python or R and querying languages such as SQL.
Experience designing and evaluating experiments or other causal inference approaches, and using metrics to assess model and product impact.
Experience collaborating in cross‑functional settings (for example, with product, engineering, or operations teams) to deliver and operationalize data science solutions at scale.
Or an equivalent combination of education and experience.

Responsibilities

Lead end‑to‑end data science projects from problem framing and hypothesis design through model development, validation, and deployment, ensuring solutions are aligned with strategic business goals and customer needs.
Develop advanced statistical and machine learning models (for example, prediction, recommendation, or optimization) using large, complex datasets, and rigorously evaluate performance to improve accuracy, fairness, and robustness.
Design and implement efficient data pipelines, feature engineering approaches, and experimentation frameworks in partnership with data engineering and product teams to enable scalable, repeatable analyses and model delivery.
Translate complex quantitative findings into clear, compelling insights and recommendations for technical and non‑technical stakeholders, informing product roadmaps, operational decisions, and measurable business outcomes.
Collaborate closely with product, engineering, analytics, and business partners to identify high‑impact opportunities, define success metrics, and prioritize data science work based on customer value and feasibility.
Establish and advocate for best practices in data quality, model monitoring, documentation, and responsible AI usage, helping to continuously improve the reliability and trustworthiness of data‑driven solutions.
Mentor less experienced team members by sharing domain knowledge, reviewing work, and contributing to a culture of learning, experimentation, and continuous improvement in data science methods and tools.