Real Estate Data Science

Role Overview

Join the Alternatives Data Science team and lead data science and AI initiatives across the Real Estate investing platform. The role sits at the intersection of investing, AI, and analytics, partnering closely with Deal Teams, portfolio company management teams, and Engineering to identify opportunities where data and AI can improve investment decisions and drive value creation.

A key aspect of the role is acting as the bridge between investment professionals and technical teams, translating investment questions into analytical solutions and ensuring data science and AI outputs are communicated in a commercially relevant and actionable way. 

The Alternatives Data Science team works alongside Goldman Sachs Deal Teams and asset management teams across the full investment lifecycle.

Key Responsibilities

• Serve as the Data Science lead for the Real Estate investing platform, supporting origination, due diligence, asset management, and value creation activities.
• Partner with Deal Teams to identify high-value opportunities where data science, AI, and advanced analytics can enhance investment decision-making and operational performance.
• Translate commercial and investment questions into well-defined analytical problems and communicate technical outputs coherently to non-technical stakeholders.
• Lead the development of data-driven models, decision-support tools, and AI-enabled solutions across the investment lifecycle.
• Identify, evaluate, and leverage traditional and alternative datasets to generate investment insights and support underwriting, market analysis, asset monitoring, and value creation initiatives.
• Partner with portfolio company management teams to identify and implement data and AI initiatives that drive measurable business outcomes.
• Scale the bespoke analytics to be applicable to the broader investment sectors so that the solutions can be redeployed periodically.
• Stay current with developments in AI, machine learning, and data science, helping drive adoption of new capabilities across the investment platform.

Qualifications, Experience & Attributes

• MSc, PhD, or equivalent degree in Mathematics, Statistics, Economics, Engineering, Computer Science, Data Science, Operations Research, or a related quantitative discipline.
• 5+ years' experience applying data science, machine learning, AI, or advanced analytics to solve complex commercial problems with measurable business impact.
• Strong programming skills in Python and SQL, with experience building robust analytical solutions and working with large and complex datasets.
• Deep understanding of statistical modelling, machine learning, predictive analytics, and experimental design.
• Demonstrated experience working directly with senior business stakeholders, translating commercial requirements into analytical solutions and communicating technical findings to non-technical audiences.
• Experience leading analytics initiatives from problem definition through implementation and business adoption.
• Proven ability to influence decision-making and drive adoption of data-driven solutions across cross-functional teams.
• Strong stakeholder management, communication, and problem-solving skills.
• Comfortable operating in a fast-paced, dynamic environment with multiple stakeholders and competing priorities.

Highly Valued

• Experience within real estate, infrastructure, private equity, investment banking, consulting, or other data-intensive commercial environments.
• Experience supporting investment, strategy, underwriting, capital allocation, pricing, or operational decision-making through advanced analytics.
• Experience leveraging alternative data, geospatial data, market intelligence, forecasting, or predictive analytics to generate commercial insights.
• Hands-on experience with modern AI technologies, including LLMs, prompt engineering, RAG architectures, agentic workflows, embeddings, and AI-enabled productivity solutions.
• Experience building analytical products, decision-support tools, or AI applications that have influenced strategic, operational, or investment outcomes.
• Familiarity with modern cloud data platforms and technologies, including Databricks, Snowflake, AWS, Azure, or GCP.
• Experience working in embedded, investment-facing, consulting, or front-office analytics teams where business adoption and impact are critical measures of success.
• Familiarity with responsible AI practices, model governance, and evaluation frameworks.

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