Quantitative Researcher – Real Estate & Econometrics
About the position 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 a Quantitative Researcher – Real Estate & 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. Responsibilities • Develop econometric and predictive models : Use quantitative methods to identify patterns, forecast trends, and interpret economic relationships in CRE markets. • Build and optimize data workflows : Create and refine data ingestion and transformation pipelines for structured and unstructured commercial real estate datasets. • Ensure data quality : Clean, validate, and reconcile datasets from diverse sources to ensure accuracy and reliability. • Communicate insights : Translate complex quantitative analysis into clear, actionable insights for both technical and non-technical stakeholders. • Stay current : Keep up with developments in econometrics, applied analytics, and trends in CRE markets and data science. Requirements • 3+ years of experience in an econometrics-focused or analytical role (e.g., economics research, finance, market analysis, consulting, commercial real estate, or corporate strategy). • Strong understanding of econometrics, applied statistics, and quantitative modeling, demonstrated through academic or professional experience. • Proficiency in Python and familiarity with analytical/ML libraries (pandas, NumPy, scikit-learn, XGBoost). • Experience working with large or complex datasets and using data to study market dynamics. • Excellent organizational, communication, and stakeholder management skills. Nice-to-haves • Hands-on experience or interest in LLMs, embeddings, NLP tools, or modern ML frameworks (e.g., Hugging Face, LangChain, PyTorch/TensorFlow). • Experience working in commercial real estate, PropTech, or investment analytics. Apply tot his job