Quant Trader (Sports & Prediction Markets)

Responsibilities:

• Autonomous Live Valuation: Engineer the logic synthesizing real-time sports data, player metrics, and market feeds into fair-value anchors and dynamic, real-time spreads.
• Liability & Inventory Skewing: Build self-correcting models that automatically adjust odds based on book exposure and lopsided betting volume to incentivize balancing the book.
• Defensive Design & Sharp Mitigation: Implement high-velocity protocols to mitigate adverse selection from "sharp" action, court-siding, and information asymmetry in milliseconds.
• Risk Automation & Hedging: Define the algorithmic logic for warehousing sports risk internally versus routing and hedging exposure across external exchanges and market makers.
• Technical Translation: Convert complex sports pricing strategies and mathematical models into scalable requirements for data and platform engineering teams.
• Trading Desk Guardrails: Partner with the risk desk to define the engine’s operational boundaries, maximum liability thresholds, and automated kill-switches.
• Real-Time Leadership: Act as the technical anchor during high-leverage sporting events, providing rapid calibration and manual intervention when the system is under peak pressure.

Requirements:

• Quantitative Sports Fluency: Deep understanding of probability, binary prediction contracts, and sports analytics; treating every match, inning, or political event as a shifting probability curve.
• Pipeline Design: Experience building production-ready betting or trading systems, from low-latency sports data ingestion (e.g., Opta, Sportradar) to automated bet acceptance engines.
• Applied Data Science: Success deploying predictive models that learn from in-play microstructure, public betting sentiment, and price velocity.
• Systems Integrity: Expertise in building error-tolerant infrastructure that remains rock-solid under extreme throughput (e.g., Super Bowl, World Cup, or election nights).