Sr Data Scientist Real Estate

Academy•Katy, TX

5d•Onsite

About The Position

In this role, you shape data science solutions that support enterprise real estate decisions and strategic investment choices. Your work helps leaders understand new store sales potential, market opportunity, cannibalization, customer demand, portfolio performance, risk, and return on investment. By connecting analytical outputs to measurable business outcomes, you help the organization prioritize growth opportunities and tradeoffs across physical and digital channels. Working across the full analytics lifecycle, you frame business problems, prepare and integrate diverse enterprise data, develop and validate models, and support deployment, monitoring, and adoption. You will build and operationalize predictive models, analyze customer demand and market dynamics, and turn findings into executive-ready recommendations, dashboards, narratives, and decision aids. You will also provide technical guidance, peer review, and mentorship to junior data scientists while contributing to a collaborative and innovative team environment. You will partner closely with Real Estate, Finance, Marketing, and business stakeholders to evaluate revenue potential, risk, sales transfer, market saturation, portfolio performance, and ROI implications. This collaboration brings together business context and analytical rigor so leaders can make informed strategic investment decisions. Across the data science team, you will work independently and alongside others, sharing technical guidance and reviewing analytical work. You will help junior data scientists strengthen their approach while supporting a team environment centered on practical problem-solving, clear communication, and responsible adoption of new analytical and generative AI capabilities.

Requirements

Bachelor’s degree in Computer Science, Data Science, Information Systems/Sciences, Business Analytics, Machine Learning, Information Technology Management or relevant field of study required
4+ years of experience in data science, predictive modeling, machine learning, statistical analysis, experimentation, or business analytics required
Proficiency in Python for model development and analysis, including pandas and spatial workflows
Advanced capability in statistical methods, forecasting, causal inference, experimentation/A/B testing, segmentation, geospatial analysis, and predictive modeling
Ability to work with large structured and unstructured datasets, including transactional, customer, store, lease/site, market, demographic, competitive, and financial data
Advanced SQL skills required
Ability to translate ambiguous business problems and complex analysis into clear, executive-ready recommendations through concise data storytelling
Strong business acumen with the ability to connect analytical outputs to measurable enterprise outcomes
Self-motivated and able to work independently and within a team setting
Strong analytical, problem-solving, and critical-thinking skills with a bias toward practical business application
Familiarity with practical generative AI use cases, including prompt engineering, AI-assisted coding, automation, and responsible adoption to improve productivity and business outcomes
Ability to remain organized, pay strict attention to detail, and meet critical deadlines within a high-volume, fast-paced environment
A team-first attitude; while your core duties are your priority, you're happy to step in wherever you're needed
Acceptable level of hearing and vision to perform job duties
Ability to adhere to company policies and professional standards

Nice To Haves

Master’s degree preferred
Experience supporting enterprise decision-making in real estate, finance, store growth, customer analytics, or related business functions preferred
Retail or omnichannel experience preferred, with exposure to store, digital, customer, market, and financial data
Google BigQuery experience preferred with broader cloud data platform exposure

Responsibilities

Shape data science solutions that support enterprise real estate decisions and strategic investment choices.
Understand new store sales potential, market opportunity, cannibalization, customer demand, portfolio performance, risk, and return on investment.
Connect analytical outputs to measurable business outcomes.
Prioritize growth opportunities and tradeoffs across physical and digital channels.
Frame business problems, prepare and integrate diverse enterprise data, develop and validate models, and support deployment, monitoring, and adoption.
Build and operationalize predictive models.
Analyze customer demand and market dynamics.
Turn findings into executive-ready recommendations, dashboards, narratives, and decision aids.
Provide technical guidance, peer review, and mentorship to junior data scientists.
Contribute to a collaborative and innovative team environment.
Partner closely with Real Estate, Finance, Marketing, and business stakeholders to evaluate revenue potential, risk, sales transfer, market saturation, portfolio performance, and ROI implications.
Share technical guidance and review analytical work.
Help junior data scientists strengthen their approach.
Support a team environment centered on practical problem-solving, clear communication, and responsible adoption of new analytical and generative AI capabilities.