Problem: Executives lacked a unified view to predict churn risk and revenue leakage across regions.
Role: Product lead orchestrating ML engineers, data scientists, and GTM partners.
Responsibilities: Defined North Star metrics, led customer discovery in 12 markets, prioritized predictive features, and partnered with finance to model ROI.
Tech Stack: Snowflake, dbt, PyTorch forecasting models, Streamlit, Tableau embedding APIs.
Business Impact: 18% reduction in churn within six months, $42M incremental ARR, 3x faster executive reporting.
Problem: Support teams spent 40% of time searching for resolutions, impacting CSAT and SLAs.
Role: AI product owner partnering with CX leadership and research.
Responsibilities: Crafted prompt frameworks, ran grounded evals, partnered with legal on compliance, and piloted human-in-the-loop review.
Tech Stack: Azure OpenAI GPT-4, LangChain, ElasticSearch, ServiceNow integration, Datadog monitoring.
Business Impact: 35% faster resolution, +9 CSAT, $14M annual savings from reduced escalations.
Problem: Regulatory scrutiny required transparent oversight across credit underwriting models.
Role: Product strategist reporting to Chief Risk Officer.
Responsibilities: Built compliance roadmap, facilitated ethics board reviews, and prioritized remediation backlog across 7 model families.
Tech Stack: Fairlearn, SHAP explainability, Azure ML, Jira Product Discovery, Confluence playbooks.
Business Impact: Achieved SOC2 Type II compliance, reduced audit cycle time by 60%, unlocked expansion into EU markets.
