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Prediction Market Quantitative Trader

Totalis

New York, NYFull-timeSeen 1w agoStill listed 1w ago

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At a glance

Compensation
No compensation found
Location
New York, NY
Schedule
Full-time
Work Authorization
Not specified

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Job overview

Totalis is building a chain‑agnostic infrastructure for prediction markets and seeks a Quantitative Trader to develop pricing and risk systems for prediction market parlays and combo trades, working directly with the founders in an early‑stage startup environment.

Skills & qualifications

RequiredNice to have

Skills

PythonMonte Carlo SimulationCopulasGraphical ModelsBayesian NetworksMultivariate Probability ModelsProbability and StatisticsExchange MicrostructureLiquidityAdverse SelectionRFQ Quoting

Full job description

Totalis is building the derivative layer for prediction markets. We have a chain agnostic infrastructure for prediction markets. We are pioneering the financial products that become possible when derivatives can be built on top of them. The team Eric previously worked at Coinbase & Faire. Pravesh has built crypto infra across Eigen Labs, Squid Router, and other teams working on swaps, routing, and onchain systems. You will work directly with the founders on the systems that define the company. The work We are hiring a Quantitative Trader to develop and operate the pricing and risk systems behind prediction market parlays and combo trades. You will determine fair values for combos, model correlations between event contracts, set executable prices, and manage the portfolio risk. This role sits at the intersection of quantitative research, sports trading, derivatives pricing, and real-time market making. What we are looking for

  • Experience with quoting RFQs on other prediction markets (Kalshi or Polymarket)
  • Familiar with exchange microstructure, liquidity, and adverse selection
  • Strong understanding of probability and statistics
  • Experience pricing or trading sports, prediction markets, or other trad-fi derivatives
  • Experience with one or more relevant modeling approaches, such as Monte Carlo simulation, copulas, graphical models, Bayesian networks, or multivariate probability models
  • Extremely high ownership and comfort operating in an early stage startup environment

Why Totalis

  • Work on a new financial product category at the intersection of prediction markets, sports trading, and derivatives
  • Price contracts that do not yet have standardized models or established market conventions, work with proprietary cross-venue market and RFQ data
  • Join an early team building foundational infrastructure for the prediction market ecosystem

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