Job Description

Role Overview-Commercial Real Estate Decision Scientist

Fred – Hiring Manager

Ryan Rivas – Business Intelligence leader

• Translates business intelligence into actionable decisions.
• Leads real estate portfolio strategy for corporate real estate, including:
• Occupancy planning
• Portfolio utilization
• Space optimization decisions

Role: Decision Scientist (New Practice)

• Focuses on corporate real estate analytics, including:
• Real estate market analysis
• Property disposition decisions
• Space utilization optimization
• Goal of the role: build a new practice that delivers predictive insights to optimize real estate decisions.
• This is a newly initiated function this year .

Core Responsibilities

• Work closely with Data Scientists and Business Intelligence teams .
• Aggregate and interpret data to generate predictive insights .
• Partner with:
• Strategy & Planning Directors
• Occupancy Planning teams
• Support decision-making for real estate portfolio actions.

Work Environment & Structure

• Location: San Antonio, TX
• On-site requirement: 4 days per week in office
• Interview process: 2 rounds
• Strong collaboration with technical and business stakeholders.

Example Business Problem

• A lease/building is coming up for renewal with a 10-year renewal option (yes/no decision) .
• The role supports answering:
• Should we renew?
• What action should we take based on data and forecasts?

Role Expectations

• Heavy emphasis on data-driven decision-making .
• Must communicate findings clearly to both:
• Technical teams (data scientists, BI)
• Business stakeholders (strategy, real estate leadership)
• Acts as a bridge: turning complex data into consumable insights and recommendations .

Initial Work (First 6 Months)

• Contribute to 3 high-impact projects immediately .
• Focus on multiple upcoming lease decisions.
• Expected to add measurable value quickly.

Growth & Future Potential

• Role may convert to full-time employment (FTE) .
• Seen as a strategic capability investment with growth potential.
• Part of a new, expanding analytics practice in corporate real estate.

Required Real Estate Knowledge

• Understanding of:
• Commercial real estate fundamentals
• Lease structures and terms
• Financial components of real estate decisions

Must-Haves

• Strong technical ability:
• Working with complex datasets
• Converting data into predictive models and forward-looking insights

Nice-to-Haves

• Understanding of corporate structure and how large organizations make decisions

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