Quantitative Trader - Sports Prediction Markets
About Raven
Raven is a proprietary crypto market-making and high-frequency trading firm. We trade spot, perpetuals, and prediction markets with dedicated low-latency infrastructure across eight major trading regions.
We hire competitive individuals and natural problem solvers with a record of ranking at something hard, whether competitive programming, games, or mathematics, who treat an unfamiliar problem as a challenge rather than an obstacle.
We can teach markets; we cannot teach drive. We build our own stack end to end, from connectivity and pricing through execution, monitoring, and research data.
The rOLE
wHAT YOU'LL OWN
- Event models. Team and player state turned into a probability distribution, built from ratings, lineups, pace, venue and weather effects, and referee quirks. A stable of models rather than one grand unified theory, and you know exactly where each one stops working
- Quoting and market making. Two-sided prices on discrete outcomes, with spread and size that reflect how confident the model actually is, and inventory managed across outcomes that must sum to one
- News, latency, and in-play. Injuries, lineups, red cards, and delayed starts, met with state-dependent live repricing and suspension logic that withdraws quotes before adverse selection sets in
- Resolution risk. What a contract actually settles on, in the rules text and not the spirit. Real PnL, not legal boilerplate
- Risk and sizing. Position sizing that survives a bad week, correlation across events that look independent and are not, and exposure you understand before it is a problem
Who we're looking for
- Demonstrated skill forecasting real-world events for money, supported by quantitative evidence, from a betting syndicate, professional or semi-professional betting, a research seat graded on calibration, or sportsbook trading if you can demonstrate the transition described below
- You think in probabilities and know when you are wrong. You can explain how you would validate a model, what would count as evidence against it, and which of your own models you have retired
- Strong applied statistics, including Bayesian inference, hierarchical models, time-varying team strength, and enough machine learning to know when it is the wrong tool
- Data instincts. Odds histories, lineups, injuries, and results are messy and revised after the fact, and you catch the look-ahead bias before it flatters a backtest
- If you are from a sportsbook, the transition that matters is from pricing recreational flow to competing against a market. We look for evidence you can tell the difference
- Fluent English
- Crypto and exchange microstructure are a plus
- No degree required. Experience is what matters
logistics
- Sofia (Bulgaria) or Milan (Italy), with no preference between them. Whichever city the right person is in
- In office, not remote
Ready to apply?
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