New York City Metropolitan Area
As an AI Product Manager at CBRE, I oversee the delivery of key features for the GenAI-powered Deal Intelligence Assistant, supporting teams managing $149B+ in AUM. I collaborate with cross-functional teams to translate business and risk requirements into actionable deliverables, ensuring alignment across stakeholders. My contributions include configuring Azure OpenAI GPT models to enhance summarization accuracy and information retrieval. Pursuing an MBA in Strategy and Marketing Analytics at Pace University has strengthened my ability to drive data-driven decision-making. With expertise in smart document parsing, source-linked analytics, and business-to-business solutions, I am dedicated to enabling organizations to achieve operational efficiency and strategic insights through innovative AI-driven technologies.
Digital & Technology | Ellis AI Platform • Owned 3 features delivery for the Deal Intelligence Assistant, a GenAI-powered assistant that summarizes investment memos, extracts KPIs, and accelerates deal analysis for teams managing $149B+ in AUM. • Translated business and risk requirements into 40+ epics, user stories, BPMN workflows, wireframes, and acceptance criteria in JIRA, enabling clear execution alignment across engineering, data science, UX, and compliance stakeholders. • Collaborate with Data Science to configure Azure OpenAI GPT models, embeddings, and RAG pipelines to improve summarization accuracy, information retrieval, and response relevance across large unstructured document sets. • Validate and profile datasets in Snowflake (structured property data, transactions, valuations, comparables) for AI readiness, lineage, and governed access aligned with internal AI policies. • Led UAT and model validation by designing 50+ test scenarios, executing SQL-based checks, reviewing AI outputs with SMEs, and tracking usage via Azure Monitor and Power BI, reducing post-release issues by ~30% and improving adoption. • Manage sprints and releases in Azure DevOps: refine backlog, handle dependencies, communicate timelines, and coordinate cross-team delivery. • Create training content and self-serve flows using Storylane and Confluence; improved pilot-team adoption and reduced onboarding time for new users. • Track usage analytics, accuracy metrics, and feedback to support roadmap decisions and prioritization of high-value AI features.
• Supported Senior PMs in early-stage discovery and feature definition for Ellis AI use cases in Research, Capital Markets, and Portfolio Strategy. • Helped map current-state workflows and pain points into candidate user stories for AI automation (document summarization, insight extraction, data lookup). • Assisted with prompt experimentation and evaluation of GPT outputs; documented failure patterns, edge cases, and improvement opportunities. • Performed initial SQL/Snowflake checks to validate that required datasets were complete and structured for downstream AI workloads. • Contributed to internal demo decks, release notes, and onboarding guides used for Ellis AI pilot launches.
Retail & Customer Analytics (Reliance Retail) • Supported the Retail Analytics team for Reliance Retail, working with merchandising and category managers to improve sales, margin, and inventory performance using data. • Built SQL and Power BI dashboards combining point-of-sale, loyalty, and e-commerce data to provide daily/weekly views of category performance, pricing effectiveness, and promotion impact. • Developed forecasting and promotional-lift models that helped increase category revenue by ~18% and improve planning for high-impact campaigns. • Automated recurring KPI reporting for leadership, reducing manual report creation effort by ~35% and enabling faster decision cycles. • Worked with data engineers to validate ETL jobs, troubleshoot data-quality issues, and ensure consistent numbers across merchandising, finance, and supply-chain teams. • Participated in requirements workshops, helping translate business questions into measurable metrics and reporting requirements.
(Banking Payment & Credit card fraud Division) • Acted as the lead BA for the Credit Card Fraud Reissue (CCFR) platform integrating Visa/Mastercardfraud event feeds with internal transaction and customer data, enabling automatic identification and reissuance of compromised credit cards. • Designed and documented BRDs, FRDs, process flows, decision matrices, exception handling, and UI requirements for fraud alert review, reissue approval, and customer notification journeys. • Collaborated with fraud strategy, card operations, and engineering teams to configure risk scoring rules and thresholds to balance fraud detection with customer experience. • Contributed to predictive analytics and rules tuning, which improved fraud detection precision and helped reduce downstream manual investigations and false positives. • Led BA work for a digital onboarding platform handling CIP, CDD, eKYC, and AML checks for retail and small-business customers, supporting both account opening and payments activation. • Mapped end-to-end onboarding journeys including application intake, identity verification, document upload, biometric checks, risk scoring, AML triggers, review queues, and account activation. • Gathered requirements and authored BRDs/FRDs/API specifications for integrations with third-party verification providers and internal systems (core banking, fraud, customer MDM). • Led metadata and data-governance initiatives using Collibra and Alation, defining data dictionaries, critical data elements (CDEs), and lineage across source, staging, and marts for fraud and onboarding domains. • Partnered with data engineering teams to optimize ETL jobs and batch processing pipelines, achieving ~30% reduction in ETL load time and ~25% improvement in data latency for downstream analytics. • Worked with analytics teams to design Power BI/Tableau dashboards for fraud-trend monitoring, onboarding funnel analytics, operational KPIs, and regulatory reporting views.