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Home Based Real Estate Data Science Jobs (NOW HIRING)

Texas Triad Homes

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Real Estate Showing Agent - Leads Provided - Growth Opportunity

Dallas, TX · On-site

$1.5K - $30K/mo

The position will involve showing homes to buyer clients as well as assisting approximately 10-15 ... Company Description We are a Dallas-based real estate brokerage and investment company led by an ...

MIG Real Estate Services, LLC

Senior Data Analyst

$88K - $111K/yr

MIG Real Estate is looking for a Senior Data Analyst to join our growing real estate team ... Experience integrating Power BI with Excel, including CUBEVALUE-based reporting and Excel-as ...

PLACE

Real Estate Buyer's Agent

Tulsa, OK · On-site

$40K - $75K/yr

This position is perfect for agents who thrive on working closely with buyers-conducting home tours ... About-our-team About Accent Realtors Accent Realtors is a Tulsa-based real estate brokerage built ...

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Home Based Real Estate Data Science information

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$37.5K

$122.7K

$196.5K

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

As of Jul 6, 2026, the average yearly pay for home based 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 the difference between Home Based Real Estate Data Science vs Real Estate Data Analyst?

AspectHome Based Real Estate Data ScienceReal Estate Data Analyst
CredentialsDegree in Data Science, Statistics, or related field; proficiency in programming languages like Python or RDegree in Data Analysis, Economics, or related field; skills in Excel, SQL, and data visualization tools
Work EnvironmentPrimarily remote, working with real estate datasets, modeling, and predictive analyticsRemote or on-site, focusing on data interpretation, reporting, and supporting real estate decisions
Industry UsageUsed by real estate firms, tech companies, and data-driven agencies for predictive modeling and market analysisCommonly employed by real estate agencies, brokerages, and investment firms for data reporting and insights

Home Based Real Estate Data Science involves advanced modeling and predictive analytics, requiring technical expertise, while Real Estate Data Analysts focus on interpreting data and generating reports. Both roles are essential in the real estate industry but differ in complexity and scope.

More about Home Based Real Estate Data Science jobs
What cities are hiring for Home Based Real Estate Data Science jobs? Cities with the most Home Based Real Estate Data Science job openings:
What are the most commonly searched types of Real Estate Data Science jobs? The most popular types of Real Estate Data Science jobs are:
What states have the most Home Based Real Estate Data Science jobs? States with the most job openings for Home Based Real Estate Data Science jobs include:
What job categories do people searching Home Based Real Estate Data Science jobs look for? The top searched job categories for Home Based Real Estate Data Science jobs are:
Home Based Real Estate Data Science jobs near you
Infographic showing various Home Based Real Estate Data Science job openings in the United States as of June 2026, with employment types broken down into 89% Full Time, 8% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Real Estate Data Scientist - Remote

Real Estate Data Scientist - Remote

Harbor Freight Tools

Calabasas, CA • On-site, Remote

$98K - $147K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 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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