Quant Developer

Job Description

Wallex is a FinTech company providing a secure cryptocurrency exchange platform and digital asset payment solutions
With a vision to become a leading global FinTech enterprise, Wallex is recognized as one of the largest
cryptocurrency exchange platforms in Iran, supporting the online trading of over 130 cryptocurrencies.

We are looking for a Quant Developer with strong experience in live trading, market making, quantitative modeling, Python, and market microstructure to join our Market Making team.

You will work closely with the Head of Market Making to improve pricing, quoting, hedging, and trading operations across automated strategies trading real capital.

Responsibilities:
-Develop and tune pricing models, including fair value, spreads, skew, order sizing, and inventory limits.
Analyze live trading performance through markouts, adverse selection, PnL attribution, fees, and fill rates.
Model and optimize hedging, inventory risk, funding, and basis.
Build data pipelines, analytics, dashboards, and automated operational tools.
Develop realistic backtests and simulations accounting for fees, latency, and queue position.
Take strategies from research to production through staged rollouts, kill switches, and circuit breakers.
Investigate live-vs-backtest performance and continuously improve trading strategies.
Evaluate new instruments, venues, data sources, and trading tools.
Write production-grade Python cod

Qualifications:
- 3+ years of experience in a live trading role involving market making, arbitrage, liquidity provision, or execution.
Strong practical knowledge of market microstructure, order books, queue priority, adverse selection, inventory risk, fees, and exchange constraints.
Deep expertise in at least one of spot, futures, options, or on-chain markets.
Strong knowledge of professional market-making practices, including risk limits, PnL attribution, capital allocation, and kill switches.
Advanced SQL and strong experience with pandas or polars on large tick-level datasets.
Strong applied statistics knowledge, including regression, time series, distributions, and significance testing.
Strong production Python skills, including asyncio, Git, testing, and code review.
Strong ownership and ability to independently identify data and tooling requirements and drive them into production.