CompStak is a pioneer in crowdsourced commercial real estate (CRE) data and analytics. Our platform transforms raw lease, sales, and property data into actionable insights for brokers, lenders, landlords, and investors. As we expand our data products, the scale and sophistication of our data pipelines and analytical systems are critical to delivering reliable, timely, and high-quality insights to our customers.
Location: New York, NY (Hybrid- Three days per week in the office, subject to change)
We are seeking an Applied Data Scientist – Econometrics with a strong foundation in economics, econometrics, finance, or commercial real estate and an interest in applying quantitative modeling to real-world market behavior. This role is ideal for someone who understands how markets work, has experience working with data, and wants to apply (and grow) modern analytical and data science methods within a commercial real estate context.In this role, you will help shape how CompStak analyzes, models, and interprets market dynamics by combining domain expertise with econometric and quantitative techniques.
Your mission is to:
-Apply econometric and statistical modeling to analyze and forecast trends in commercial real estate and related economic drivers.
-Learn and apply modern analytical and machine learning techniques as needed to enhance insights and support CompStak’s data products.
-Build scalable workflows that integrate structured CRE datasets with new, unstructured sources to support data enrichment and automation.
-Collaborate closely with engineering, product, and data teams to develop data-driven solutions.
-Bridge domain knowledge and quantitative methods to deliver models that are accurate, interpretable, and meaningful for business decisions.
