Applied Data Scientist
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 an Applied Data Scientist - Real Estate with expertise in econometric modeling and commercial real estate (CRE). In this role, you will play a key part in shaping how we leverage data to deliver insights across the CRE industry. Your mission is to: Leverage advanced statistical modeling to analyze and predict commercial real estate analytics and market trends. Apply modern ML/AI techniques (XGBoost, embeddings, LLMs, etc.) to extract insights, and power CompStak's next-generation data products. Build scalable pipelines that integrate structured CRE datasets with structured and unstructured sources, improving ingestion, enrichment, and automation. Collaborate across engineering, product, and data teams to bring innovative, data-driven solutions to life. Bridge data and domain expertise by bringing real estate/finance context into models, ensuring accuracy, interpretability, and business relevance.
Requirements
- 3+ years of experience in an economics-focused role (e.g., corporate position in technology, finance, commerce, or market analysis).
- Strong understanding of economics, demonstrated through professional or academic experience.
- Proficiency in Python and key ML libraries (scikit-learn, XGBoost, PyTorch/TensorFlow).
- Excellent organizational, communication, and stakeholder management skills.
Nice To Haves
- Hands-on experience with LLMs, embeddings, or NLP frameworks (e.g., Hugging Face, LangChain).
- Experience working with commercial real estate investment firms or within the PropTech industry.
Responsibilities
- Create predictive models: Apply statistical and machine learning methods to identify patterns, forecast trends, and generate actionable insights in CRE markets.
- Build and optimize pipelines: Develop, improve, and automate large-scale data ingestion and transformation pipelines for commercial real estate datasets.
- Ensure data quality: Validate, clean, and reconcile data from diverse sources to maintain accuracy and reliability.
- Communicate insights: Translate complex analyses into clear, actionable recommendations for both technical and non-technical stakeholders.
- Stay ahead of trends: Keep up to date with the latest advancements in CRE industry dynamics, data science, and ML frameworks.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Industry
Real Estate
Education Level
No Education Listed
Number of Employees
101-250 employees
