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Real Estate Data Science Internship information

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How much do real estate data science internship jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for real estate data science internship in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

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

AspectReal Estate Data Science InternshipReal Estate Data Analyst
Required CredentialsCurrently pursuing or recently completed degree in data science, statistics, or related fieldBachelor's or master's in data analysis, statistics, or related field
Work EnvironmentInternship programs within real estate firms, tech companies, or consulting agenciesFull-time or part-time roles in real estate firms, property management companies, or investment firms
Employer & Industry UsageUsed for training, skill development, and entry-level exposure in real estate data projectsUsed for ongoing data analysis, reporting, and decision-making in real estate operations

The main difference is that a Real Estate Data Science Internship is an entry-level, temporary position focused on learning and supporting data science projects, while a Real Estate Data Analyst role is a full-time position involving ongoing data analysis and reporting. Internships are ideal for gaining experience, whereas analyst roles involve more responsibility and independence in analyzing real estate data.

What is a real estate data science internship?

A Real Estate Data Science Internship is a temporary position where students or recent graduates work with real estate companies or related organizations to analyze property data, identify trends, and support business decisions using data-driven methods. Interns typically use tools like Python, R, and SQL to process and visualize data, and may work on projects involving market analysis, property valuation, or predictive modeling. This internship provides hands-on experience in applying data science techniques to real estate challenges, helping interns build valuable skills for future careers in both fields.

What are the key skills and qualifications needed to thrive as a real estate data science intern, and why are they important?

To excel as a Real Estate Data Science Intern, you need a solid background in statistics, data analysis, and programming, often supported by coursework or a degree in data science, statistics, computer science, or a related field. Familiarity with tools such as Python, R, SQL, and data visualization platforms like Tableau, as well as experience working with real estate datasets or geographic information systems (GIS), is highly valuable. Strong problem-solving, communication, and teamwork skills help you interpret data effectively and collaborate with stakeholders. These capabilities are crucial for delivering actionable insights that inform real estate investment, development, and strategy decisions.

What types of projects or analyses do real estate data science interns typically work on during their internship?

As a Real Estate Data Science Intern, you may work on projects such as analyzing property market trends, building predictive models to estimate property values, or optimizing investment portfolios using large datasets. Daily responsibilities often include data cleaning, exploratory data analysis, and collaboration with real estate analysts and engineering teams. These experiences help interns develop practical skills and gain exposure to the intersection of data science and real estate, paving the way for future career growth in both fields.
More about Real Estate Data Science Internship jobs
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Real Estate Data Science Internship jobs near you
Infographic showing various Real Estate Data Science Internship job openings in the United States as of August 2026, with employment types broken down into 6% Internship, 72% Full Time, and 22% Part Time. Highlights an 100% In-person job distribution, with an average salary of $46,809 per year, or $22.5 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 17 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.
Requirements
Education and Experience
Education Requirements
  • Bachelor's degree in data science, Statistics, Economics, Mathematics, Computer Science, or related field from a nationally recognized institution. Master's degree preferred
Years of Experience
  • 5 to 8 plus years of experience in data science, analytics, or quantitative modeling, preferably in retail, real estate, consulting, or related fields.
Skills
  • Strong proficiency in Python (pandas, scikit-learn, etc.) and SQL for data analysis and modeling.
  • Experience building predictive models and applying statistical techniques to business problems.
  • Experience working with cloud-based platforms (Databricks, Snowflake, Azure)
  • Familiarity with geospatial analysis and GIS concepts, including trade area modeling, spatial statistics, and network-based analysis (experience with ESRI or similar tools preferred).
  • Experience working with large, complex datasets from multiple sources.
  • Experience with BI and visualization tools (Tableau, Power BI, etc.).
  • Strong understanding of experimental design and statistical inference.
  • Ability to communicate complex analyses clearly to non-technical stakeholders.
  • Strong problem-solving skills and business acumen.
Physical Requirements
Corporate - Remote - General office environment requiring ability to:
  • Stand, walk, sit for extended periods of time .
  • Speak and listen to others in person and over the phone and video conferencing.
  • Use keyboard and read from computer screen and reports.
  • The ability to lift up to 15 lbs.
Safety
Must be able to perform this job safely in accordance with standard operating procedures and good manufacturing practices, without endangering the health or safety of self or others.
About Harbor Freight Tools
We're a 45 year-old, $8 billion national tool retailer with the energy, enthusiasm, and growth potential of a start-up. We have over 1,600 stores in 48 states across the country and are opening several new locations every week. We offer our customers more than 7,000 tools and accessories, from hand tools and generators to air and power tools, from shop equipment to automotive tools. We provide our customers with the right tool for the right job at the right price, always delivering quality and value.

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