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Evening Real Estate Data Science Jobs (NOW HIRING)

NOV, Inc.

Real Estate Analyst

Houston, TX · On-site

This role will assist with real estate portfolio data management, reporting, project coordination, and real estate-related administrative processes across NOV's global portfolio of leased and owned ...

National Oilwell Varco

Real Estate Analyst

Houston, TX · On-site

This role will assist with real estate portfolio data management, reporting, project coordination, and real estate-related administrative processes across NOV's global portfolio of leased and owned ...

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 ... Bachelor's degree in a field related to data science, or equivalent practical experience. * Minimum ...

Parker's Kitchen

Real Estate Analyst

Savannah, GA · On-site

The ideal candidate thrives in a data-driven environment, communicates insights clearly, and has a strong understanding of the convenience store, fuel retail, or broader retail real estate landscape.

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

As of Jul 5, 2026, the average yearly pay for evening 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 an Evening Real Estate Data Scientist?

An Evening Real Estate Data Scientist is a professional who applies data science techniques to analyze and interpret real estate data, typically working during evening hours. Their responsibilities often include examining property trends, evaluating investment opportunities, and creating predictive models to assist real estate companies or clients in making informed decisions. Working in the evenings can accommodate clients across different time zones or support organizations that operate outside standard business hours. These data scientists need strong analytical, statistical, and programming skills, often using tools like Python, R, and SQL. Their insights help optimize property valuation, marketing strategies, and operational efficiencies in the real estate sector.

What are the typical collaboration dynamics for an Evening Real Estate Data Science role?

In an Evening Real Estate Data Science role, you will often work closely with real estate analysts, property managers, and IT professionals to deliver data-driven insights outside of standard business hours. This unique schedule allows you to process and analyze large datasets with minimal interruptions while providing timely updates for teams starting their day. Regular communication is maintained via digital channels, and you may participate in virtual meetings or handoff sessions to ensure seamless workflow continuity. Collaboration is key, so strong remote communication and documentation skills are essential for success in this role.

What are the key skills and qualifications needed to thrive as an Evening Real Estate Data Scientist, and why are they important?

To excel as an Evening Real Estate Data Scientist, you need strong analytical skills, experience in statistical modeling, and a background in real estate, typically supported by a degree in data science, computer science, or a related field. Proficiency with tools like Python, R, SQL, and real estate analytics platforms such as CoStar or Zillow is essential, as well as familiarity with machine learning frameworks. Excellent problem-solving, communication, and time management skills help you translate complex data into actionable business insights, especially when working independently or on flexible evening schedules. These competencies are critical for providing accurate, timely analysis that supports strategic decision-making in the real estate sector.
What cities are hiring for Evening Real Estate Data Science jobs? Cities with the most Evening 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 Evening Real Estate Data Science jobs? States with the most job openings for Evening Real Estate Data Science jobs include:
Evening 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 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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