AI Property Valuation & Market Insights Analyst

Job Summary

Santriya Technologies is seeking an AI property valuation & market insights analyst to produce property market and valuation insights using automated valuation models and AI analytics as decision support, with clear data quality, assumptions and human review.

Product & documentation operating model

You will be assigned one or more company products, client solutions or operational services. The approved product documentation, SOPs, policies, process maps, knowledge articles, release notes, controls, scripts and AI runbooks are the source of truth. Use only approved enterprise AI tools to retrieve, summarize, draft, classify, prioritize or automate work. Validate material AI outputs against current documentation and authoritative system data, record exceptions, and elevate conflicts or missing guidance.

Do not invent product features, prices, customer entitlements, engineering limits, regulatory positions or safety instructions.

Responsibilities

• Analyze property attributes, comparable evidence, transaction/listing trends and local market drivers.
• Interpret AVM/model outputs, confidence and limitations rather than presenting a model estimate as a formal valuation when it is not one.
• Use AI to summarize local-market evidence and identify comparable candidates, verifying source data.
• Investigate outliers, data quality issues and model disagreement.
• Prepare client/partner/internal market insight reports with sources and assumptions.
• Monitor model performance and segment bias with data/model teams.
• Protect licensed, personal and confidential property data.
• Escalate cases requiring regulated valuation or specialist surveyor judgement.

AI-enabled ways of working

• Use approved GenAI copilots, enterprise search/RAG, analytics and workflow agents to reduce repetitive work and improve decision preparation.
• Keep prompts, outputs and automated actions within approved data-access, confidentiality and retention rules.
• Correct inaccurate summaries, classifications or recommendations before they become customer, operational or system records.
• Feed recurring AI errors, knowledge gaps and process friction into product, documentation and control improvement.

Human accountability

AI is a copilot, not the accountable decision-maker. The role holder remains responsible for judgement, quality, privacy, security and appropriate escalation. Credit, fraud blocking, legal/regulatory, financial advice, safety-critical, clinical, employment and other high-impact decisions must follow delegated authority and required human review.

Success measures

• Insight turnaround improves
• Model limitations are communicated clearly
• Data quality issues are detected earlier
• Users receive evidence-backed market context