Applied Scientist

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About the team Join our dynamic team, leveraging a comprehensive database of over 100 million homes, transactions, and users to predict and analyze housing market trends. As an Applied Scientist specializing in traditional econometrics and machine learning with a focus on time series and hierarchical forecasting, you will play a critical role in shaping our understanding and forecasting of the housing market. About the role As an Applied Scientist you will actively participate in Collaborative Forecast Development: Work alongside other applied scientists, data scientists and economists to develop hierarchical forecasts for the housing market at various regional levels: Feature Extraction: Identify economic and demographic driving forces of housing market growth across regions and extract features that predict housing market trends 1 to 2 years in the future. Model Deployment: Collaborate closely with the engineering team to deploy models into production environments. Forecast Production: Contribute to monthly forecast production, communicating forecast performance and the housing market outlook to business partners and senior leadership team. Scenario Modeling: Develop scenario models that capture a wide range of housing market outcomes and contribute to a company-wide stress-testing framework. 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.In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $139,400.00 - $222,600.00 annually. This base pay range is specific to these locations and may not be applicable to other locations.In Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia the standard base pay range for this role is $132,400.00 - $211,600.00 annually. The base pay range is specific to these locations and may not be applicable to other locations.In addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location. Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside. Who you are Keen Housing Market Observer: Strong interest and understanding of the housing market and the economic factors that influence it. Innovative Problem Solver: Passionate about working on innovative solutions to time series, panel data, and hierarchical forecasting problems, conducting independent research, and applying research methods to real-world problems. Technical Proficiencies: Strong foundation in traditional econometric methods and modern machine learning techniques. Proficient in data cleaning, preprocessing, and feature engineering for time series data. Experience with large-scale datasets and familiarity with distributed computing frameworks such as Spark. Ability to design and implement robust model evaluation and validation strategies, including cross-validation and backtesting. Experience with metrics for time series forecasting accuracy and performance assessment. Proficient in cloud-based platforms and tools for deploying and monitoring machine learning models. Proficient in building AI agent to automate manual work Communication Skills: Excellent verbal and written communication skills, capable of conveying complex concepts to a broad audience. Experience: 2+ years of proven experience working in time series/spatial forecasting space. Educational Background: Master’s or PhD degree in Mathematics, Statistics, Economics, Econometrics, Physics, Earth Sciences, or a related scientific field Get to know us At Zillow, we’r

关于团队 加入我们充满活力的团队，利用涵盖超过1亿套房屋、交易和用户的综合数据库来预测和分析住房市场趋势。作为一名应用科学家，专注于传统计量经济学和机器学习，并侧重于时间序列和分层预测，您将在塑造我们对住房市场的理解和预测方面发挥关键作用。 关于该职位 作为一名应用科学家，您将积极参与协作式预测开发：与其他应用科学家、数据科学家和 economist 一起，为不同区域层级的住房市场开发分层预测：特征提取：识别各区域住房市场增长的经济和人口驱动因素，并提取可预测未来1至2年住房市场趋势的特征。模型部署：与工程团队紧密合作，将模型部署到生产环境中。预测生产：参与月度预测生产，向业务合作伙伴和高级领导团队沟通预测表现和住房市场展望。情景建模：开发情景模型，以捕捉广泛的住房市场结果，并为公司范围内的压力测试框架做出贡献。该职位被归类为远程职位。“远程”员工没有固定的公司办公场所，而是在其选择的实际地点工作，且该地点必须向公司确认。美国员工可以居住在美国50个州中的任何一个州，但有有限例外。在加利福尼亚州、康涅狄格州、马里兰州、马萨诸塞州、新泽西州、纽约州、华盛顿州和华盛顿特区，该职位的标准基本薪资范围为每年$139,400.00 - $222,600.00。该基本薪资范围仅适用于这些地点，可能不适用于其他地点。在科罗拉多州、夏威夷州、伊利诺伊州、缅因州、明尼苏达州、内华达州、俄亥俄州、罗德岛州、佛蒙特州和弗吉尼亚州，该职位的标准基本薪资范围为每年$132,400.00 - $211,600.00。该基本薪资范围仅适用于这些地点，可能不适用于其他地点。除了具有竞争力的基本薪资外，该职位还可根据经验、绩效和地点等因素获得股权奖励。实际金额将因经验、绩效和地点而异。担任该职位的员工薪资不会低于其所在州豁免员工薪资门槛。 您是谁 敏锐的住房市场观察者：对住房市场及其影响因素的经济因素有浓厚兴趣和深入理解。创新的问题解决者：热衷于针对时间序列、面板数据和分层预测问题开发创新解决方案，开展独立研究，并将研究方法应用于现实世界问题。技术能力：在传统计量经济学方法和现代机器学习技术方面具有扎实基础。熟练进行时间序列数据的数据清洗、预处理和特征工程。具有大规模数据集经验，并熟悉 Spark 等分布式计算框架。能够设计和实施稳健的模型评估与验证策略，包括交叉验证和回测。具有时间序列预测准确性和性能评估指标的经验。熟练使用基于云的平台和工具来部署和监控机器学习模型。熟练构建 AI agent 以自动化人工工作。沟通能力：出色的口头和书面沟通能力，能够向广泛受众传达复杂概念。经验：2年以上在时间序列/空间预测领域经过验证的工作经验。教育背景：数学、统计学、经济学、计量经济学、物理学、地球科学或相关科学领域的硕士或博士学位。 了解我们 在 Zillow，我们

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