Real-Time Edge Analytics Platform for Democratized Alpha

Challenge

Retail traders drown in headline latency and opaque signals, while institutional-grade streams remain gated behind high fees and NDAs. MarketReader needed a real-time analytics layer that meets FINRA fair-disclosure standards and scales to millions of concurrent users without exchange-rule violations.

Strategy

Build a high-throughput ingestion mesh that fuses multi-venue trade data with alt-data (news sentiment, options flow) in milliseconds.
Deploy an online feature store plus auto-retraining ML pipelines to detect micro-alpha opportunities as they emerge.
Add an explainability layer—Shapley value scoring + narrative synthesis— so every alert shows causal factors regulators can audit.
Package outputs into a tiered API and white-label widgets for broker integration (web & mobile).

Execution

Negotiated exchange and alt-data contracts, cutting raw-feed latency to <5 ms hop.
Led a cross-functional “LLM platform coalition”: data engineers, quants, and product designers collaborated on feature pipelines and UI surfacing.
Implemented model-risk governance: bias tests, drift alerts, and kill switches wired into CI/CD.
Launched tiered pricing—free delayed feed, real-time retail, and pro API— backed by partner GTM playbooks.

Outcomes

Achieved sub-200 ms end-to-end signal latency from tick to alert.
Secured three retail-broker integrations at launch, putting the platform in front of 2.4 million active accounts.
Transitioned revenue mix from 100 % one-off deals to 65 % recurring SaaS within two quarters.
Passed FINRA disclosure review with zero findings, thanks to built-in explainability.

Key Capabilities Demonstrated

Chief Product & Data Strategy leadership
High-throughput data architecture & auto-retraining ML pipelines
Explainable-AI design that satisfies regulatory scrutiny
Pricing & GTM frameworks for scalable SaaS subscriptions