Data Scientist

Required Skills & Experience
• \t5+ years of experience in data science, machine learning, advanced analytics, or a similar applied quantitative role.\n•\tStrong Python experience.\n•\tStrong SQL experience.\n•\tExperience building and applying machine learning or statistical models to real business problems.\n•\tExperience with traditional ML techniques such as regression, random forest, XGBoost, classification, forecasting, optimization, or similar approaches.\n•\tAbility to explain technical concepts and model outputs to non-technical stakeholders.\n•\tExperience working with messy or incomplete business requirements and shaping them into analytical solutions.\n•\tComfort working in a smaller, collaborative, fast-moving team.\n•\tAbility to work onsite in Burbank, CA.\nPreferred Skills\n•\tGoogle Cloud Platform experience.\n•\tExperience with pricing, revenue management, marketing analytics, customer lifetime value, web analytics, real estate analytics, retail, travel, hospitality, consumer behavior, or marketplace-style business models.\n•\tExperience productionizing models or analytical tools.\n•\tFamiliarity with CI/CD, APIs, automation, or MLOps concepts.\n•\tExposure to LLMs or AI-enabled workflows.\n•\tExperience modernizing Excel-based processes into scalable data products or models.\n•\tExperience with Jupyter Notebooks, VS Code, cloud data environments, or similar tools.\n•\tExposure to AWS SageMaker or third-party model integration is a plus.\n•\tTensorFlow or PyTorch exposure is nice to have, but not required.

Job Description

A reputable REIT (Real Estate Investment Trust) company is looking for hands-on, applied Data Scientists to join a growing data and analytics team supporting high-impact business initiatives across pricing, revenue management, marketing, customer analytics, website conversion, and real estate decision-making.\nThis is an opportunity to join a maturing analytics organization where Data Scientists will have meaningful ownership, direct business impact, and the ability to help shape best practices. The ideal candidate is not purely academic or theoretical. This person should be comfortable working across data preparation, modeling, analytics, automation, and stakeholder communication to deliver practical, scalable solutions.\nKey Responsibilities\n•\tBuild, improve, and deploy data science solutions that support pricing, revenue management, marketing, customer behavior, and real estate investment decisions.\n•\tTranslate ambiguous business problems into data-driven approaches, models, and actionable insights.\n•\tUse Python and SQL to prepare data, develop models, validate results, and generate recommendations.\n•\tPartner with business stakeholders to understand objectives, frame analytical questions, and explain model outputs in clear business terms.\n•\tImprove existing analytical tools, models, and Excel-based processes into more scalable and production-ready solutions.\n•\tDevelop machine learning models using techniques such as regression, random forest, XGBoost, and other traditional ML approaches.\n•\tSupport model explainability and help non-technical users understand model logic, outputs, and business implications.\n•\tCollaborate with data engineering resources to access data, prepare datasets, and support pipelines.\n•\tWork with cloud-based tools, primarily within Google Cloud Platform.\n•\tContribute to automation, CI/CD practices, and repeatable deployment processes for analytical and machine learning solutions.\n•\tExplore and support AI/LLM-related use cases as the organization continues to mature its AI strategy.