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

As of Jun 30, 2026, the average yearly pay for remote 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.

How to make $80,000 a year working from home?

A remote real estate data scientist can earn $80,000 or more annually by developing expertise in data analysis, machine learning, and real estate markets, often requiring proficiency in tools like Python, R, or SQL. Building a strong portfolio, gaining relevant certifications, and working for established companies or consulting firms can help achieve this income level while working remotely.

Can I get a remote job as a data scientist?

Remote data scientist positions are widely available across various industries, including real estate data science roles. These jobs typically require strong skills in programming, statistical analysis, and data visualization, and often involve using tools like Python, R, or SQL. Many companies offer flexible schedules and fully remote work environments for qualified candidates.

What is the difference between Remote Real Estate Data Science vs Remote Real Estate Analyst?

AspectRemote Real Estate Data ScienceRemote Real Estate Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; knowledge of programming languages like Python or RDegree in Real Estate, Business, or related field; strong analytical skills
Work EnvironmentFocus on data modeling, machine learning, and predictive analyticsFocus on market analysis, property valuation, and reporting
Employer & Industry UsageTech companies, real estate platforms, data-driven firmsReal estate agencies, investment firms, brokerage companies

Remote Real Estate Data Science involves advanced data modeling and machine learning to predict market trends, while Remote Real Estate Analyst focuses on analyzing property data and market conditions. Both roles require analytical skills but differ in technical complexity and focus areas.

What is the 80 20 rule in data science?

In data science, including roles like remote real estate data scientists, the 80/20 rule suggests that roughly 80% of results come from 20% of the efforts or data. It helps prioritize features, data cleaning, and analysis to focus on the most impactful variables or insights for better model performance.

Is 40 too late for data science?

In the field of remote real estate data science, age is not a barrier to entry. Success depends on acquiring relevant skills such as programming, statistical analysis, and data visualization, which can be developed at any age through online courses and certifications. Many professionals transition into data science later in their careers and find opportunities in the industry.
What cities are hiring for Remote Real Estate Data Science jobs? Cities with the most Remote 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 Remote Real Estate Data Science jobs? States with the most job openings for Remote Real Estate Data Science jobs include:
Remote Real Estate Data Science jobs near you
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 4 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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