zillow
Website:
zillow.com
Job details:
About The Team
The business forecasting group at Zillow shapes planning across Zillow’s businesses. We bring together forecasters, economists, applied scientists, data scientists, and data visualization engineers to build and maintain the forecasts that guide decision-making. We translate housing market and business signals into actionable outlooks with industry-leading data and rigorous statistical methods. We emphasize explainability with our forecasts so that leaders can have confidence and make informed decisions about the future of the company
Zillow Group is a strategic, mission-driven organization focused on delivering exceptional experiences and measurable outcomes. Our work spans cross-functional partnership, scalable programs and operational excellence in support of Zillow’s mission. We bring deep experience working across diverse teams in a dynamic, high-growth environment, balancing strategic thinking with hands-on execution to drive meaningful business impact. We are seeking an experienced professional to support our workforce expansion in India.
About The Role
Zillow is seeking an Applied Scientist based in India to focus full-time on developing and improving forecasting performance for Zillow’s business lines..
In this role, you will design, implement, and refine advanced statistical and machine learning approaches to generate accurate, scalable forecasts. You will partner with team members to expedite and improve the quality of modeling work. Further, you will collaborate closely with cross-functional teams to translate complex data into actionable insights.
You Will Get To:
- Design and evaluate statistical, econometric, and machine learning methods for forecasting problems. Your work will play a central role in increasing forecast accuracy and accelerating delivery timelines
- Propose and evaluate novel methods to reasonably forecast asset level performance
- Build backtesting and validation frameworks to assess forecast accuracy, stability, and downstream forecast impact.
- Own the end‑to‑end modeling lifecycle, including scoping, feature engineering, model development, experimentation, deployment, monitoring, and model explainability.
- Translate forecasts into clear insights and recommendations for senior leadership, helping stakeholders understand drivers, uncertainty, and trade‑offs that guide Zillow’s strategy.
- Quantify uncertainty and clearly communicate model confidence, limitations, and trade-offs to technical and non-technical audiences.
- Partner cross‑functionally with finance, product, engineering, marketing, and operations to scale and improve forecasting capabilities across Zillow.
- Improve and contribute to shared forecasting tools, data pipelines, and processes used across the company.
- Collaborate with other applied scientists and data scientists to develop novel solutions to real estate and business problems.
This role has been categorized as an Office position. “Office” employees regularly work at the Zillow India office for approximately 80 to 100 percent of their time each month. Employees must live within a reasonable commuting distance of the office. Zillow has not defined a reasonable distance, and expects employees will use judgment in determining this for themselves and understand the implications re: time commitment and cost of daily commute.
In addition to a competitive base pay, employees in this role are eligible for incentive compensation subject to applicable laws and relevant Zillow policies. Actual amounts will vary depending on experience, performance and location.
Who you are
- Advanced degree (PhD or Masters) in Economics, Statistics, Operations Research, Data Science, Computer Science, Econometrics, Mathematics, or a related quantitative discipline or a related quantitative discipline with 5+ years of experiences in professional applied scientist roles
- Strong experience with time-series forecasting, econometrics, or related quantitative modeling techniques.
- Strength in data engineering principles to help envision efficient data solutions at scale.
- Experience explaining complex models and analytical concepts to stakeholders with non-technical backgrounds using clear takeaways and practical business framing.
- Experience building applied statistical or machine learning models that support real business decisions in production settings.
- Proficient in Python and SQL for data analysis, model development, validation, and deployment.
- Comfortable working with incomplete, delayed, or noisy real-world data and designing robust estimation strategies.
Get to know us
At Zillow, we’re reimagining how people move—through the real estate market and through their careers.
As the most-visited real estate platform in the U.S., Zillow helps millions of customers navigate buying, selling, financing, and renting with greater ease and confidence. Our teams in India play a critical role in building and scaling the technology, products, and operations that power this experience.
Whether you’re working in tech, operations, or shared services, you’ll collaborate with global partners to solve meaningful problems and help more people make home a reality.
Zillow is honored to be recognized among the best workplaces in the U.S.. Zillow was named one of FORTUNE 100 Best Companies to Work For® in 2025, and included on the PEOPLE Companies That Care® 2025 list, reflecting our commitment to creating an innovative, inclusive, and engaging culture where employees are empowered to grow.
No matter where you sit in the organization, your work will help drive innovation, support our customers, and move the industry—and your career—forward, together.
Zillow Group is an equal opportunity employer committed to fostering an inclusive, innovative environment with the best employees. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, maternity status, HIV status, or veteran status. Reasonable accommodations will be provided to candidates with disabilities, in accordance with applicable policies.
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