Quantitative Developer

Storm2; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Storm2 is seeking a quantitative developer for a client in the United States. You will work directly with traders to translate pricing ideas and trading logic into production‑grade Python systems that support pricing, execution, and risk management in fast‑moving markets.

The role blends research and engineering, requiring hands‑on development with market data, backtesting frameworks, and production deployment.

Qualifications

• Advanced Python development skills and experience working with market or order book datasets.
• Strong understanding of probability, statistics, model validation, and backtesting practicalities.
• Experience working directly with traders to translate requirements into robust software.
• Track record of taking projects from idea through deployment and iteration.
• Experience in sports trading, exchange betting, sportsbook pricing, or market making.

Responsibilities

• Partner directly with traders to translate pricing and trading strategies into production‑ready Python systems.
• Build fair value models, pricing engines, quoting logic, and inventory management tools.
• Develop simulation and back testing frameworks that reflect live market conditions.
• Integrate strategies into production trading environments.
• Monitor live performance and investigate unexpected system or market behaviour.
• Analyze liquidity, order flow, market microstructure, and portfolio exposure.
• Work with engineering and trading teams to improve execution quality and reliability.

Skills

Advanced Python

Trading experience

Statistics & model validation

Market data / order book

Idea-to-production delivery

Job description

The Company

Storm2's client is a profitable prediction market trading firm with a live trading desk and a growing engineering team. Much of the operation has historically relied on manual processes, and they're now investing heavily in the systems, tooling, and automation needed to scale while keeping traders in control of decision-making.

The Role

Most quantitative developer roles sit somewhere between research and engineering without really owning either. This one does.

You’ll work directly with traders, taking pricing ideas, market‑making strategies, and trading logic from concept through to live production systems. The challenge is building software that can support pricing, execution, and risk management in fast‑moving sports and prediction markets, while behaving reliably when real money is on the line.

This will suit someone who’s already worked in sports betting, exchange betting, sportsbook pricing, or market making and enjoys the process of turning ideas into production‑grade systems. You’ll see your work deployed, measured, and iterated on quickly.

What you’ll be working on:

• Partnering directly with traders to translate pricing and trading strategies into production‑ready Python systems.
• Building fair value models, pricing engines, quoting logic, and inventory management tools.
• Developing simulation and back testing frameworks that accurately reflect live market conditions.
• Integrating strategies into production trading environments.
• Monitoring live performance and investigating unexpected system or market behaviour.
• Analyzing liquidity, order flow, market microstructure, and portfolio exposure.
• Working closely with engineering and trading teams to improve execution quality and operational reliability.

What you’ll bring:

• Experience in sports trading, exchange betting, sportsbook pricing, or systematic sports betting.
• Advanced Python development skills and experience working with market or order book datasets.
• A strong understanding of probability, statistics, model validation, and the practical limitations of backtesting.
• Experience working directly with traders and translating trading requirements into robust software.
• A track record of taking projects from initial idea through deployment and ongoing iteration.

Especially relevant experience:

• Sports market making and automated quoting.
• In‑play or in‑game pricing.
• Betting exchanges or sportsbook trading environments.
• Fair value modelling, exposure management, and capital allocation.
• Execution systems, order book data, and market microstructure.