[Hiring] Senior Data Scientist @Zillow

Role Description

As a Senior Data Scientist working in ZEXP, you'll deliver value through your analyses and by elevating the work of the team around you. You'll bring statistical rigor to two connected problems:

• Making Zillow's experimentation practice more self-service and trustworthy.
• Measuring whether Zillow's growing investment in AI-assisted engineering and development tools creates durable, high-quality value rather than just activity.

This is an experienced individual-contributor role that blends hands-on measurement, causal inference, and applied statistics with team leadership. You'll act as a focal point for projects, mentor other scientists and analysts, and represent data science and experimentation to partner teams — advocating for sound measurement wherever decisions get made. You'll balance depth and speed in an environment of competing priorities, and you'll know when further rigor stops adding value.

You will get to:

• Design, build, and validate statistically rigorous methods for experiment design and analysis — productionizing techniques such as variance-reduction and sequential testing as reusable, self-service platform capabilities.
• Build and improve automated guardrails that prevent the common causes of experiment restarts and manual interventions — in-flight monitoring and alerting, ramp-plan adherence, early-stopping thresholds, and cross-line-of-business guardrail-harm checks.
• Lead experiment design reviews for high-visibility launches, catching issues like ill-suited experimental populations, misaligned hypotheses, or contradictory launch criteria.
• Define and scale a measurement framework for AI's impact on engineering and behavior — segmenting usage and proficiency at the team and org level, and connecting adoption to behavioral change, productivity, quality, and financial outcomes.
• Pair velocity signals with lagging quality measures (reverts, change-failure rate, MTTR, defects, maintainability) so that speed gains are never mistaken for net value.
• Apply causal-inference thinking to AI product and tooling measurement — establishing sound, defensible methods for measuring whether AI investments deliver real value.
• Turn complex analyses into leadership-ready narratives and self-service insight experiences that partners can explore on their own.
• Mentor junior scientists and analysts on the core data science toolkit, share best practices across the team, and lead workstreams end to end.

This role has been categorized as a teleworker position. Teleworkers 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. Employees may live anywhere in Mexico, with availability to travel to Mexico City, as we recommend attendance at occasional office events.

In addition to a competitive base salary and benefits, this position is also eligible for equity awards based on factors such as experience, performance and location.

Qualifications

• PhD and 2+ years, MS and 4+ years, or BS and 6+ years of experience in Statistics, Econometrics, Data Science, Machine Learning, Operations Research, or a related quantitative field.
• Demonstrated track record of owning analyses end to end and turning them into decisions that partners trust.

Requirements

• Strong applied background in online experimentation and causal inference — frequentist and Bayesian methods, experimental and quasi-experimental designs.
• Familiar with the common failure modes of experimentation platforms (e.g., sample ratio mismatch, experiment interference, temporal exposure bias, multiple comparisons).
• Experienced with measuring productivity or product quality from telemetry — comfortable defining metrics, documenting their limitations, and resisting the urge to over-claim causality from cross-sectional observational data.
• A clear communicator who tells a persuasive story with data and tailors the level of technical detail to the audience, from engineers to executives.
• Proficient in Python and SQL, with relevant libraries (e.g., NumPy, pandas, scikit-learn); comfortable moving between different tools, codebases, and data sources.
• A trusted mentor and partner who raises the bar on best practices and advocates for data science across disciplines without alienating others.
• Curious about AI/ML measurement — LLM-assisted analysis, agentic tools, and how to evaluate them rigorously — a strong plus.

Benefits

• Competitive base salary.
• Benefits package.
• Equity awards based on experience, performance, and location.