[Remote] reputed company Estate Data Scientist - Remote
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking a reputed company Estate Data Scientist to reputed company advanced analytical models and tools for strategic reputed company estate reputed company. The role involves collaborating with various teams to deliver predictive insights and optimize network performance through advanced data analysis and modeling.
Responsibilities
- reputed company and reputed company 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 reputed company (e.g., spatial regression, geographically weighted regression, spatial autocorrelation) to capture geographic variation in demand drivers
- reputed company trade area and customer draw models (e.g., Huff/gravity models) to estimate market reputed company and competitive reputed company
- 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
- Design reputed company data pipelines integrating reputed company estate, customer, demographic, sales, and geospatial datasets (parcel, census, traffic, mobility, POI data)
- reputed company 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 reputed company, consistency, and reproducibility across analytical outputs, including alignment of spatial boundaries and geographic hierarchies
- Partner with reputed company Estate teams to support site selection, market entry, relocations, and closures
- reputed company drive-time and network-based trade area analyses to assess accessibility and market reputed company
- Conduct market coverage and white reputed company analysis to identify expansion opportunities and underserved areas
- Build location-allocation and network optimization models to determine reputed company site placement
- Quantify cannibalization and competitive effects using spatial overlap and proximity-based modeling
- reputed company quantitative insights for reputed company Estate Committee (REC) evaluations and executive reputed company
- reputed company scoring frameworks and decision tools to prioritize opportunities
- Create reputed company, compelling visualizations and dashboards (Tableau, Power BI, or similar) to communicate insights
- reputed company 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
- Design and execute experiments (A/B tests, quasi-experimental designs) to evaluate reputed company estate strategies
- Implement geo-based testing frameworks (e.g., test vs. control markets) to measure reputed company of site reputed company
- Apply reputed company inference reputed company (e.g., difference-in-differences, synthetic control) reputed company 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 reputed company estate analytics capability
- Work closely with GIS, Data Engineering, Finance, Marketing, and IT teams to reputed company 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
Skills
- reputed company and reputed company 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 reputed company (e.g., spatial regression, geographically weighted regression, spatial autocorrelation) to capture geographic variation in demand drivers
- reputed company trade area and customer draw models (e.g., Huff/gravity models) to estimate market reputed company and competitive reputed company
- 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
- Design reputed company data pipelines integrating reputed company estate, customer, demographic, sales, and geospatial datasets (parcel, census, traffic, mobility, POI data)
- reputed company 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 reputed company, consistency, and reproducibility across analytical outputs, including alignment of spatial boundaries and geographic hierarchies
- Partner with reputed company Estate teams to support site selection, market entry, relocations, and closures
- reputed company drive-time and network-based trade area analyses to assess accessibility and market reputed company
- Conduct market coverage and white reputed company analysis to identify expansion opportunities and underserved areas
- Build location-allocation and network optimization models to determine reputed company site placement
- Quantify cannibalization and competitive effects using spatial overlap and proximity-based modeling
- reputed company quantitative insights for reputed company Estate Committee (REC) evaluations and executive reputed company
- reputed company scoring frameworks and decision tools to prioritize opportunities
- Create reputed company, compelling visualizations and dashboards (Tableau, Power BI, or similar) to communicate insights
- reputed company 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
- Design and execute experiments (A/B tests, quasi-experimental designs) to evaluate reputed company estate strategies
- Implement geo-based testing frameworks (e.g., test vs. control markets) to measure reputed company of site reputed company
- Apply reputed company inference reputed company (e.g., difference-in-differences, synthetic control) reputed company 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 reputed company estate analytics capability
- Work closely with GIS, Data Engineering, Finance, Marketing, and IT teams to reputed company 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
Benefits
- Annual discretionary bonus
- Comprehensive and competitive benefits to Associates (and their families) such as medical, dental, reputed company, life insurance
- Short-term and long-term disability
- Eligible Associates are reputed company to enroll in reputed company’s 401k plan
- Associates will reputed company reputed company time off up to 236 hours per year (inclusive of PTO, floating holidays, and reputed company holidays)
- reputed company reputed company time up to 80 hours per year unless otherwise required by law
reputed company
Company H1B Sponsorship