AI Property Valuation & Market Insights Analyst (Chennai)

- 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 explicit 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 escalate conflicts or missing guidance. - Do not invent product features, prices, customer entitlements, engineering limits, regulatory positions or safety instructions. - Key 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 .