How SIG Engineers Meritocracy

What’s it like being a trader at SIG? At Susquehanna International Group, Todd Simkin has trained some of the world’s best traders using poker strategy, probabilistic games, and decision-making under uncertainty that mirror real-world quantitative trading. In this episode, Todd breaks down how SIG teaches trading interns Bayesian updating, asymmetric information, market microstructure awareness, and communication under pressure. From reading opponents at the poker table to interpreting order flow as a market-making trader, he explains how these game-theoretic models build trading intuition, strengthen probabilistic judgment, and sharpen the edge required for systematic trading and derivatives trading.Todd also dives into the traits that distinguish exceptional traders from simply intelligent ones — humility, truth-seeking, and the ability to update beliefs quickly when new information arrives. He explains how SIG screens for these qualities in interviews, what it’s like working or interning at SIG, and why technical skill alone isn’t enough for options pricing or market-making. Todd breaks down how real meritocracy works inside a flat trading desk, how traders collaborate to refine ideas, and how the best quants learn to think critically, debate openly, and iterate their decision process rather than operate alone.How market-making works at SIG and how traders interpret order flow, liquidity, and real-time signalsWhat Bayesian updating looks like in practice during a live trading sessionHow trading systems, not just individuals, drive performance in modern quantitative tradingThe structure of SIG’s pods and how traders collaborate inside a flat trading deskWhy communication and idea-sharing matter more than hierarchy in quantitative researchInsights into SIG’s interview process, including probabilistic reasoning, game situations, and ambiguity testsWhy SIG prioritizes truth-seeking culture and “attacking ideas, not people” in decision-makingWhat it’s like interning at SIG and how long-term projects reveal real trading aptitudeHow SIG evaluates technical skill vs. judgment vs. adaptability in new hiresWhy systematic trading requires parameter tuning, model monitoring, and rapid belief updatesHow traders combine options pricing, market microstructure, and private information to form an edgeThe role of sports trading, insurance risk, and prediction markets inside SIG’s broader ecosystemHow SIG thinks about risk transfer, volatility events, and pricing uncertaintyWhy Susquehanna moves into new businesses only when it can be best-in-classThe philosophy behind SIG’s expansion into prediction markets (Kalshi, PolyMarket, etc.)The economics of risk indemnification, NIL deals, promotions, and event-driven insuranceHow traders apply game-theoretic optimal reasoning beyond poker — in pricing, hedging, and model design