Applied Data Scientist, CompStak
Real Estate
$120-160k
3 days a week in office
Commercial real estate data platform
Be an early applicant
Commercial real estate data platform
101-200 employees
Be an early applicant
$120-160k
3 days a week in office
101-200 employees
Company mission
To revolutionize the commercial real estate industry by bringing transparency to clients.
Role
Who you are
- 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
- Bonus: Hands-on experience with LLMs, embeddings, or NLP frameworks (e.g., Hugging Face, LangChain)
- Bonus: Experience working with commercial real estate investment firms or within the PropTech industry
What the job involves
- 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
- 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
- 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
Our take
When it comes to renting commercial property and office spaces, many businesses face a lack of pricing transparency. CompStak leverages world-class commercial real estate data to solve this problem.
CompStack has enjoyed considerable success by marketing its data, which gives users a more reliable idea of commercial property values, to landlords, real estate agents and tenants alike. CompStack has also benefited from being one of the first wave of proptech companies that appeared in the 2010s. Since then it has become an established force in the market, with clients including Allianz and PwC.
The company has substantial funding behind it, which is clear proof that the it has maintained its solid footing in the proptech market. To stay ahead of emerging competitors, it will be essential for the company to leverage this money to drive further innovation and continue building on its success.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
47% employee growth in 12 months
Company
Funding (last 2 of 8 rounds)
Nov 2021
$50m
SERIES C
May 2019
$12m
SERIES B
Total funding: $98.3m
Company HQ
Chelsea, New York, NY
Leadership
Michael Mandel
(Co-Founder & CEO)Previously worked as New York Metro Leader at Grubb & Ellis, and has seen a TechStars mentor since 2016.
Vadim Belobrovka
(Co-Founder & CTO)Previously worked as a Tech Team Lead at UBS, and as a Software Engineer at Corbin Capital Partners. Then joined CRT Capital Group as Senior Software Engineer.
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