We are seeking a highly specialized Data Scientist / Analytics Consultant to support a project focused on revamping real estate data stack and pricing accuracy.
About Project: Our client specializes in delivering real-time Investor Pricing Ranges to agents and investors using a combination of AVMs, third-party data providers, and internal logic. As the platform scales, ensuring data quality, consistency, and pricing accuracy is critical to marketplace trust, conversion, and transaction volume.
What will you do? —Conduct a comprehensive audit of existing real estate data providers and infrastructure; —Benchmark and validate the performance of various AVMs and property data feeds; —Analyze current underwriting and valuation models, identifying gaps, biases, or inefficiencies in pricing logic; —Refine renovation assumptions and their impact on final pricing outputs; —Deliver a clear, actionable roadmap and hands-on adjustments to improve the reliability of Investor Pricing Range; —Collaborate closely with internal product and engineering teams to ensure swift implementation of your recommendations.
What do we expect? —High proficiency in Python (Pandas, NumPy, Scikit-learn); —Hands-on experience integrating and evaluating standard real estate APIs and data providers (e.g., MLS feeds, ATTOM, CoreLogic, HouseCanary, SmartZip, or similar); —Experience working with spatial data and GIS tools (e.g., PostGIS, GeoPandas, H3); —Deep understanding of real estate investment metrics, including ARV (After Repair Value), Cap Rates, Yield; —Proven track record of building, tuning, or auditing predictive pricing models (e.g., XGBoost, LightGBM, CatBoost, Random Forest, or other ensemble methods applied to real estate); —Ability to build clear dashboards (Tableau, Looker, PowerBI, etc.); —At least Upper-Intermediate English level (speaking & writing).
