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Real Estate Data Science Jobs (NOW HIRING)

CoStar

Data Scientist

Richmond, VA

Commercial Real Estate industry professionals around the globe use CoStar to access the most ... Bachelor's degree in computer science, data analytics, statistics, machine learning, or related ...

CoStar Group

Data Scientist

Richmond, VA · On-site

Commercial Real Estate industry professionals around the globe use CoStar to access the most ... Bachelor's degree in computer science, data analytics, statistics, machine learning, or related ...

CoStar Group

Data Scientist

Richmond, VA · On-site

Commercial Real Estate industry professionals around the globe use CoStar to access the most ... Bachelor's degree in computer science, data analytics, statistics, machine learning, or related ...

Measurabl

Principal Product Manager

Work closely with data science and engineering to translate real estate domain requirements into specs, models, and evaluation frameworks they can build and test against * Drive alignment across ...

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Real Estate Data Science information

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How much do real estate data science jobs pay per year?

As of Aug 18, 2026, the average yearly pay for real estate data science in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a real estate data scientist?

A Real Estate Data Science job involves analyzing property-related data to identify trends, predict market movements, and optimize investment decisions. Professionals in this field use machine learning, statistical modeling, and big data techniques to assess property values, rental yields, and risk factors. They work with real estate firms, investors, and lenders to make data-driven decisions, improve portfolio performance, and enhance market strategies.

What are the typical daily responsibilities of a real estate data scientist?

As a Real Estate Data Scientist, your daily tasks often include collecting, cleaning, and analyzing property and market data to identify trends and generate predictive models. You'll collaborate with real estate analysts, agents, and business leaders to translate data-driven insights into actionable recommendations, such as pricing strategies or investment opportunities. Additionally, you may develop dashboards, automate reporting processes, and stay updated on emerging data sources or analytical techniques relevant to the real estate industry. This role offers the chance to have a direct impact on business outcomes while working in a dynamic, team-oriented environment.

What are the key skills and qualifications needed to thrive as a real estate data scientist?

To excel in Real Estate Data Science, you need strong analytical abilities, a background in statistics or economics, and experience with data modeling or machine learning, often supported by a relevant degree. Mastery of tools such as SQL, Python or R, and familiarity with GIS software or real estate databases is highly valued, along with certifications like Certified Analytics Professional (CAP) being a plus. Effective communication, critical thinking, and cross-functional teamwork skills distinguish top performers in this field. These qualifications enable professionals to extract actionable insights from complex real estate data, directly driving informed business decisions and market strategies.

How is data science used in real estate?

In real estate, data science is used by professionals to analyze market trends, predict property values, and assess investment risks. It involves techniques like machine learning, statistical modeling, and data visualization to support decision-making and optimize property transactions.
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Real Estate Data Science jobs near you
Infographic showing various Real Estate Data Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Real Estate Data Scientist - Remote

Harbor Freight Tools

Calabasas, CA • On-site, Remote

$98K - $147K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 22 days ago

Job description

The Real Estate Data Scientist is responsible for developing advanced analytical models and data-driven tools that support strategic real estate decisions across the organization. This role partners closely with Real Estate, Finance, Marketing, and Supply Chain teams to deliver predictive insights related to site selection, network optimization, sales forecasting, and market planning. This role incorporates advanced spatial modeling, geostatistics, and geospatial data engineering to evaluate trade areas, quantify market potential, and optimize network performance.
This position combines strong statistical modeling, data engineering, and business acumen to translate complex data into actionable recommendations. The Real Estate Data Scientist plays a critical role in advancing the organization's use of machine learning, automation, and predictive analytics to improve decision quality and scalability. This is a senior individual contributor role with no direct people management responsibility.
Duties and Responsibilities
  • Advanced Analytics & Predictive Modeling
    • Develop and deploy predictive models for site selection, sales forecasting, cannibalization, and market potential.
    • Build and maintain machine learning models using regression, classification, clustering, optimization, and spatial modeling techniques.
    • Apply spatial statistical methods (e.g., spatial regression, geographically weighted regression, spatial autocorrelation) to capture geographic variation in demand drivers.
    • Develop trade area and customer draw models (e.g., Huff/gravity models) to estimate market share and competitive impact.
    • Incorporate spatial features such as proximity, co-tenancy, demographics, traffic patterns, and nearby store performance into predictive models.
    • Design methodologies for forecasting store performance under various scenarios, including spatial and competitive effects.
    • Continuously monitor and improve model performance and accuracy. 
  • Data Engineering & Automation
    • Design scalable data pipelines integrating real estate, customer, demographic, sales, and geospatial datasets (parcel, census, traffic, mobility, POI data).
    • Perform geospatial data processing including geocoding, spatial joins, coordinate transformations, and spatial indexing (e.g., H3 or similar frameworks).
    • Write efficient SQL and Python workflows to automate recurring analyses, spatial feature engineering, and model refreshes.
    • Ensure data quality, consistency, and reproducibility across analytical outputs, including alignment of spatial boundaries and geographic hierarchies.
  • Real Estate Strategy & Decision Support
    • Partner with Real Estate teams to support site selection, market entry, relocations, and closures.
    • Develop drive-time and network-based trade area analyses to assess accessibility and market reach.
    • Conduct market coverage and white space analysis to identify expansion opportunities and underserved areas.
    • Build location-allocation and network optimization models to determine optimal site placement.
    • Quantify cannibalization and competitive effects using spatial overlap and proximity-based modeling.
    • Provide quantitative insights for Real Estate Committee (REC) evaluations and executive decisions.
    • Develop scoring frameworks and decision tools to prioritize opportunities.         
  • Visualization & Communication
    • Create clear, compelling visualizations and dashboards (Tableau, Power BI, or similar) to communicate insights.
    • Develop interactive geospatial visualizations including trade area maps, performance heatmaps, and market opportunity analyses.
    • Present analytical findings and recommendations to senior leadership and non-technical stakeholders.
  • Experimentation & Innovation
    • Design and execute experiments (A/B tests, quasi-experimental designs) to evaluate real estate strategies.
    • Implement geo-based testing frameworks (e.g., test vs. control markets) to measure impact of site decisions.
    • Apply causal inference methods (e.g., difference-in-differences, synthetic control) accounting for geographic spillovers.
    • Explore new data sources (e.g., mobility, foot traffic) and modeling techniques to enhance predictive capabilities.
    • Contribute to building a best-in-class real estate analytics capability.
  • Cross-Functional Collaboration
    • Work closely with GIS, Data Engineering, Finance, Marketing, and IT teams to align data and models.
    • Partner with GIS teams to ensure alignment between spatial analysis, mapping, and production data pipelines.
    • Translate business problems into analytical solutions and actionable insights.
Scope
  • Staff supervision and development:  No
  • Decision making: 
    • Develops models and analytical frameworks used in strategic decision-making
  • Travel:  Up to 10%
  • Flex Designation:  Anywhere

The anticipated salary range for this position is $98,500-$147,800 depending on location, knowledge, skills, education and experience. This position is also eligible for an annual discretionary bonus. In addition, we offer comprehensive and competitive benefits to Associates (and their families) such as medical, dental, vision, life insurance, short-term and long-term disability. Eligible Associates are able to enroll in our company's 401k plan. Associates will accrue paid time off up to 236 hours per year (inclusive of PTO, floating holidays, and paid holidays). Paid sick time up to 80 hours per year unless otherwise required by law.

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