We are looking for a Quant Trader/ Quantitative Researcher to support our trading team by developing mathematical models, analyzing large datasets, and designing algorithmic trading strategies across digital asset spot and derivatives markets. You will work closely with traders, engineers, and product teams to identify inefficiencies, optimize execution, and contribute to the build-out of systematic strategies and risk models.

You'd be joining a global digital-asset proprietary trading firm operating across major centralized exchanges. Our team runs multiple strategies, including market making, arbitrage, and systematic trading where both technology and quantitative research play a central role in our performance.

•  Research, design, and prototype systematic trading strategies across spot, perp, and funding markets.
• Identify alpha opportunities using statistical, ML-based, or microstructure-driven approaches.
• Analyze exchange data (order books, trades, liquidations, funding rates, volatility) to detect patterns and market regimes.
• Develop predictive models for price movements, volatility surfaces, order flow imbalance, and liquidity conditions.

• Build robust backtesting frameworks that accurately model exchange mechanics (fees, funding, latency, depth, liquidation rules, etc.).
• Run simulations to evaluate performance, edge stability, and risk exposure under different market regimes.
• Validate strategy assumptions with sensitivity and stress tests.

• Implement models into production trading systems in collaboration with engineering.
• Monitor live strategy performance and adjust parameters according to market dynamics.
• Contribute to execution systems, including smart routing, hedging logic, and risk overrides.

• Build and maintain models for PnL attribution, risk exposure, slippage, and tail events.
• Develop monitoring tools for leverage, liquidity crunches, and liquidation cascades.
• Provide insight into market microstructure risks across CeFi and DeFi venues.

• Track exchange-level changes (funding mechanisms, fee structures, margin rules).
  
• Analyze competitor markets, price indexes, and cross-exchange spreads.
  
• Support internal teams (Product, Lending, Derivatives, Strategy) with quantitative insights.

• Strong programming skills in Python, C++, or Rust (Python required).
  
• Familiarity with distributed computing, large dataset handling, and time-series databases.
  
• Knowledge of probability, statistics, optimization, and numerical methods.
  
• Experience with algorithmic trading, HFT, or market-making strategies.

• Experience with digital assets, perpetual futures, funding rates, liquidations, index/mark prices, or CEX/DEX microstructure is highly preferred.
• Understanding of leverage/margin systems and risk models is a plus.
• Prior work on cross-exchange arbitrage, statistical arbitrage, execution algorithms, or options/vol surfaces is beneficial.

• Highly analytical and comfortable with ambiguity.
  
• Ability to translate mathematical ideas into production-level code.
  
• Strong communication skills for working with Traders, Engineers, and Product teams.
  
• Self-driven, curious, and excited to solve complex quantitative problems.
  
  

• Experience with reinforcement learning or deep learning applied to trading.
  
• Contributions to open-source quant/trading libraries.
  
• Background in cryptography, distributed systems, or blockchain networks.
  
• Experience with real-time systems or low-latency infrastructure.
• Degree in Math, Physics, CS, Engineering, Statistics, or related discipline.
• At least 5 years of experience in a quant, algo trading, or data science role.

• Remote-first company - we enable you to work from anywhere in the world.
• Flexible working hours - We have core working hours (11 am–3 pm CET), allowing flexible scheduling outside those hours.
• 38 days of paid vacation leave per year +14 days of paid sick leave
• Join a forward-thinking team where you have the autonomy to make your own choices and explore new ideas.

• Stage 1: Screening with TA Partner: basic information about ICEO, the project, the role, and the offer. General questions about your experience (about 30 mins).
• Stage 2: Interview with Senior Trading Analyst, focusing on domain check - trading strategies, order execution, modeling, analysis, etc. (30 mins)
• Stage 3: Interview with Head of Technology, focusing on tech and infra understanding - coding skills,  order routing, algos, etc. (30 mins)
• Stage 4: Final Interview with CEO of the venture, focusing on overall company and culture fit (30 mins)