smartrent · Phoenix, Arizona
Skills: Python, SQL
Who We Are SmartRent (NYSE: SMRT) is revolutionizing how people live and work with the industry’s only end-to-end platform designed for the rental housing industry. By uniting purpose-built software, integrated hardware and full implementation and support in one ecosystem, we help owners and operators simplify operations, cut costs and deliver exceptional resident experiences. Recognized by Deloitte, HousingWire and the PropTech Breakthrough Awards, SmartRent is shaping the future of property technology and redefining what it means to make rental housing smarter. Job Description As Lead Data Scientist, you will define and execute the analytics and predictive data roadmap for SmartRent’s next-generation offerings, starting with decision-support models and machine learning for optimal property operations. You will spearhead the model development lifecycle from stakeholder collaboration to deployment within a Databricks environment. Reporting to the SVP of Enterprise Data and AI in our new and transformational organization, you will combine quantitative rigor with practical problem-solving to deliver measurable value to internal and external stakeholders. You will partner with Data, AI, Product, and Engineering peers to solve our most complex challenges. Responsibilities Frame business problems with Product and Revenue partners and turn them into modeling approaches with clear success metrics. Create compelling data visualizations that support insightful story telling and inform the design of predictive models. Build machine-learning models on large-scale time-series telemetry, including anomaly detection, failure prediction, and time-to-event models. Develop clean, well-documented code that promotes reuse and drives operational efficiency within the organization. Ship models to production on Databricks, and support their quality after launch: monitoring, diagnosing drift, and retraining. Partner with Engineering to scale and harden production workloads. Design experim…
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