Real Estate Data Science, Vice President

Overview

Join the Alternatives Data Science team to lead data science and AI initiatives for the Real Estate investing platform. You will bridge investment professionals and technical teams, turning investment questions into analytical solutions and delivering actionable AI outputs. You’ll collaborate with Deal Teams, portfolio company management, and Engineering across the investment lifecycle to drive value creation. The role combines investing, AI, and analytics to enhance decision-making at scale. You will stay ahead of AI advances and scale solutions across sectors to maximize impact.

Responsibilities

• Act as the Data Science lead for the Real Estate investing platform across origination, due diligence, asset management, and value creation
• Partner with Deal Teams to identify high-value data science and AI opportunities that improve investment decision-making and operations
• Translate commercial and investment questions into well-defined analytical problems and present outputs to non-technical stakeholders
• Lead development of data-driven models, decision-support tools, and AI-enabled solutions across the investment lifecycle
• Evaluate and leverage traditional and alternative datasets to generate investment insights for underwriting, market analysis, asset monitoring, and value creation
• Collaborate with portfolio company management to implement data and AI initiatives that drive measurable business outcomes
• Scale bespoke analytics for broader deployment across investment sectors
• Stay updated on AI, ML, and data science developments to drive adoption across the platform

Key requirements

• 5+ years applying data science, ML, AI, or advanced analytics to solve complex business problems with measurable impact
• Strong Python and SQL programming skills with experience building robust analytical solutions
• Deep understanding of statistical modelling, ML, predictive analytics, and experimental design
• Experience interfacing with senior business stakeholders and translating requirements into analytical outcomes
• Proven track record leading analytics initiatives from problem definition to implementation and adoption
• Ability to influence decision-making and drive data-driven adoption across cross-functional teams
• Strong stakeholder management, communication, and problem-solving skills
• Comfort in fast-paced environments with multiple stakeholders and priorities

• Stakeholder management and communication
• Cross-functional collaboration
• Business acumen and problem-solving
• Python
• SQL
• Statistical modelling

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