Real Estate Data Science, Vice President

Goldman Sachs; Greater London; Presencial; GBP 90 000 - 150 000; Tempo integral

Resumo da oferta

Goldman Sachs is seeking a data science leader to drive AI initiatives for the Real Estate investing platform. You will partner with Deal Teams, portfolio management, and Engineering to translate investment questions into analytical solutions and actionable insights.

As Data Science lead, you will develop models and AI-enabled tools across the investment lifecycle, evaluate data sources, and communicate technical outputs to senior stakeholders in business terms.

Qualificações

• MSc/PhD or equivalent in a quantitative field.
• 5+ years applying data science, ML, AI, or advanced analytics to solve commercial problems with measurable impact.
• Strong Python and SQL; experience handling large/complex datasets.
• Deep understanding of statistical modelling, ML, predictive analytics, and experimental design.
• Experience translating business requirements into analytical solutions and communicating findings to non-technical audiences.
• Experience leading analytics initiatives from problem definition to business adoption.
• Proven ability to influence decision-making and drive adoption of data-driven solutions.

Responsabilidades

• Lead Data Science for the Real Estate investing platform across origination to value creation.
• Partner with Deal Teams to identify high-value AI opportunities and enhance decision-making.
• Translate commercial/investment questions into well-defined analytical problems and communicate outputs to stakeholders.
• Lead development of data-driven models, decision-support tools, and AI-enabled solutions.
• Identify and leverage datasets to generate investment insights and support underwriting and asset monitoring.
• Collaborate with portfolio company management to implement data/AI initiatives with measurable impact.
• Scale analytics for broader investment sectors and drive adoption across platforms.
• Stay current with AI/ML developments and promote adoption across the investment platform.

Conhecimentos

Python

SQL

Statistics

Machine Learning

Data Science

Stakeholder management

Formação académica

MSc/PhD or equivalent in quantitative field

Ferramentas

Databricks

Snowflake

AWS

Azure

GCP

Descrição da oferta de emprego

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.

About Goldman Sachs

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