AI Product Manager - Feasibility Underwriting Invest

The AI Product Manager, Feasibility, Underwriting and Investment owns the AI products that help a global real-estate business decide where, what and when to invest. The role turns market research, site screening, feasibility studies, financial underwriting and investment-committee work into agentic AI tools that make decisions faster, more consistent across markets and fully traceable. The post holder combines deep real-estate investment experience with product management skills. They decide what to build, write the requirements AI engineers deliver, and prove through evidence that each product improves decision quality. Investment decisions always stay with people and the investment committee; the products give them better evidence, sooner.

The candidate will have responsibilities across the following functions:

Product strategy:

• Own the product vision, roadmap and backlog for AI in market research, site acquisition, feasibility, underwriting and investment approval.
• Prioritise opportunities by value to investment decisions, data availability, risk and ease of adoption.
• Assess build, buy and partner options across real-estate data, valuation and investment-analytics vendors.

Market and site intelligence:

• Define AI agents that collect and summarise market evidence: comparable sales and rents, supply pipelines, absorption, pricing trends and demographics.
• Specify site-screening tools that test land and assets against zoning, planning, access, infrastructure and the company's investment criteria.

Feasibility and underwriting:

• Define agents that draft feasibility studies, including highest-and-best-use, product mix, pricing and phasing options.
• Page 1 of 4. Specify underwriting tools for development appraisals, residual land value, IRR, NPV, equity multiple and profit on cost.
• Automate sensitivity, scenario and probability analysis on sales prices, absorption, construction cost, financing cost, currency and timing.
• Lead the creation of standard underwriting templates and assumption libraries so that deals in different markets can be compared like-for-like.

Investment committee and due diligence:

• Define agents that draft investment papers and risk summaries, with every figure traced to its source and model version.
• Specify challenge tools that test a deal's assumptions against benchmarks and past deals before it reaches the committee.
• Define due-diligence agents that review legal, technical, environmental and commercial documents in data rooms and flag material issues.

Evidence and learning:

• Back-test products on past deals, comparing what was assumed, what was approved and what actually happened.
• Feed actual cost, schedule and sales results from delivered projects back into assumptions and models.
• Run new products in parallel with the existing process before they are used for live decisions.

Governance:

• Apply model risk management: model inventory, validation, version control and change logs.
• Protect confidential and price-sensitive deal information, and respect market-data licensing terms and data-protection laws.
• Make sure no product approves an investment; approvals stay with the committee and delegated authorities.

Requirements:

• Bachelor's degree in Finance, Economics, Real Estate, Business, Engineering or a related field.
• At least 10 years' experience, including 6 or more in real-estate investment, acquisitions, underwriting, feasibility or development appraisal.
• Page 2 of 4Hands-on experience building and challenging development appraisals and investment models.
• Experience preparing investment-committee papers and managing acquisition due diligence.
• At least 3 years in product management, analytics or investment-technology roles, with one or more data or AI products delivered into live use.
• Experience of investing across more than one country or region.

Desirable:

• Master's degree such as an MBA or MSc in Real Estate Finance.
• Professional qualification such as CFA, MRICS or CAIA.
• Experience with real-estate funds, joint ventures or capital raising.
• Knowledge of valuation standards (e. g. RICS Red Book, IVS) and ESG or climate-risk underwriting.

Technical Skills:

• Investment modelling: expert in Excel-based development and investment models; familiar with appraisal software (e. g. Argus).
• Market and location data: real-estate data providers, transaction databases and GIS tools for site analysis.
• Quantitative methods: forecasting, regression, scenario and Monte Carlo analysis, and model validation.
• AI fluency: how large language models, retrieval, document intelligence and AI agents work, and where they fail.
• Product delivery: writing requirements and acceptance criteria, Agile methods and backlog tools (e. g. Jira).
• Data: basic SQL, data-quality assessment and dashboard tools (e. g. Power BI, Tableau).
• Responsible AI: explainability, model risk management and data protection (e. g. GDPR).

Core Competencies:

• Investment judgement: knows what a committee needs to see and what changes a decision.
• Analytical rigour: challenges every assumption, including the AI's, and asks where each number comes from.
• Comfort with uncertainty: thinks in ranges and probabilities, not single-point answers.
• Page 3 of 4Influence: persuades senior investors with evidence, including when it contradicts their view.
• Discretion: handles confidential and price-sensitive information with care.
• Standard-setting: brings different markets to one consistent way of underwriting.
• Global outlook: follows capital-market and local real-estate trends across regions.