Data Scientist

SmartRent • Phoenix, AZ

1d • Hybrid

About The Position

As a Data Scientist at SmartRent, you will work at the intersection of the company’s property technology platform and advanced analytics. You will transform IoT telemetry, resident behavior signals, and operational data into models and decision-support tools that improve outcomes for operators, residents, and SmartRent teams. You will build and deploy production-grade machine learning solutions, partnering with Data Engineering, Product, Software Engineering, and business stakeholders to take models from experimentation into live product features. The Data & Analytics team is part of SmartRent’s Data and AI organization, which transforms data into intelligence for SmartRent products and customers.

The role focuses on developing machine learning and predictive analytics for product and operational use cases, working with the Databricks platform and large structured and semi-structured datasets, and integrating models into production applications. The preferred candidate is based in Phoenix, Arizona, and works from the Phoenix headquarters on a hybrid schedule.

Requirements

• Bachelor’s degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field, or equivalent professional experience.
• At least 3 years of hands-on experience in a data science or quantitative analytics role.
• Strong programming proficiency in Python, including experience with production-grade codebases.
• Proven experience training and evaluating supervised and unsupervised machine learning models.
• Advanced SQL skills and comfort working with large, complex, and disparate data sources, including event-based and time-series data.
• Experience with Databricks or an equivalent cloud-based lakehouse or big data platform, such as Spark or Delta Lake.
• Experience with NLP techniques, including text preprocessing, entity recognition, and sentiment analysis.
• Familiarity with standard data formats such as CSV, JSON, XML, and Parquet.
• Strong written and verbal communication skills, including the ability to present technical results to non-technical stakeholders.

Nice To Haves

• PySpark experience.
• Master’s degree or higher in a quantitative discipline.
• Hands-on experience with Databricks ML features, including MLflow, Databricks AutoML, Feature Store, Model Serving, and Delta Live Tables.
• Experience in proptech, real estate technology, IoT, or SaaS platform environments.
• Familiarity with IoT data patterns, including time-series telemetry, device state machines, and event stream processing.
• Experience building recommender systems or personalization engines for resident- or operator-facing products.
• Exposure to model risk management frameworks or machine learning governance practices, including model cards and lineage tracking.
• Experience working with Snowflake data platforms.
• Experience working directly with executive or senior leadership stakeholders.

Responsibilities

• Design, build, and validate supervised and unsupervised machine learning models for product and operational use cases, including predictive maintenance, resident churn prediction, and smart access anomaly detection.
• Build and manage machine learning workflows in Databricks, including experiment tracking, Delta Live Tables feature pipelines, and governed access through Unity Catalog.
• Conduct exploratory data analysis, feature engineering, and statistical hypothesis testing across device telemetry, resident events, leasing data, and support interactions.
• Develop and maintain predictive analytics solutions, including capacity-planning tools and resident experience scores, to inform product features and operator decisions.
• Monitor deployed model performance, retrain models as needed, and document model drift and remediation actions to maintain production reliability.
• Collaborate with Data Engineering to integrate machine learning solutions into SmartRent’s data platform and downstream product surfaces.
• Partner with Software Engineering to move models from experimentation into production APIs and embedded product features.
• Write clean, documented, reusable code and contribute to shared libraries and machine learning tooling.
• Work with Product and business stakeholders to translate business problems into precise analytical questions.
• Present findings, model outputs, and recommendations to non-technical audiences, including senior leadership.
• Contribute to data governance, data quality standards, and related documentation.
• Respond to ad hoc analysis requests and help build a culture of data-driven decision-making.

Benefits

• Medical, dental, vision, and life insurance with low deductibles and 75–100% employer contributions for US employees.
• Flexible and generous paid time off.
• Competitive 401(k) with employer contributions.
• Paid parental leave.
• Discounted pet insurance and legal services.
• Employee stock purchase plan.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level