ZipRecruiter

Log In

1

Real Estate Data Science Internship Jobs in California

Faire

Data Science Intern

San Francisco, CA

$75/hr

Data Science Internship - Fall 2026 Faire leverages machine learning and data insights to transform ... Build real-time and streaming data pipelines that enable dynamic, context-aware personalization at ...

Visa Hunt

Real Estate Portfolio Manager

San Francisco, CA · On-site

$247.50 - $302.50/hr

Create executive-level presentations and decision memos that translate complex real estate data into clear strategic recommendations * Conduct in-depth market analyses including competitive ...

New

Faire

Data Science Intern

San Francisco, CA · On-site

$75/hr

Data Science Internship - Fall 2026 Faire leverages machine learning and data insights to transform ... Build real-time and streaming data pipelines that enable dynamic, context-aware personalization at ...

next page

Showing results 1-20

All JobsReal Estate Data Science JobsReal Estate Data Science Internship JobsReal Estate Data Science Internship Jobs in California

Real Estate Data Science Internship information

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 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.

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 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 are the most commonly searched types of Real Estate Data Science jobs in California?

The most popular types of Real Estate Data Science jobs in California are:

What are popular job titles related to Real Estate Data Science Internship jobs in California?

For Real Estate Data Science Internship jobs in California, the most frequently searched job titles are:

What job categories do people searching Real Estate Data Science Internship jobs in California look for?

The top searched job categories for Real Estate Data Science Internship jobs in California are:

What cities in California are hiring for Real Estate Data Science Internship jobs?

Cities in California with the most Real Estate Data Science Internship job openings:

Real Estate Data Science Internship jobs near you
Infographic showing various Real Estate Data Science Internship job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

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 24 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.

Apply