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Remote role and mid-level seniority increase applicants, but niche quant specialization limits density.
Role requires specialized quant trading and portfolio skills, limiting cross-industry transferability.
Explicit 5+ years plus mandatory quant, trading system, and production ML/engineering skills create stringent filters.
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Design, build, and deploy 100+ systematic quantitative trading strategies annually.
Develop and maintain backtesting, optimization, and portfolio construction frameworks with focus on risk-adjusted returns.
Integrate machine learning models into trading workflows and deploy research into production trading environments involving multiple asset classes (equities, futures, forex, crypto).
5+ years of Python development experience with strong knowledge of Pandas, Polars, NumPy.
Experience in quantitative finance, portfolio optimization, risk management, and working with futures, forex, equities, or crypto trading.
Proficiency with backtesting frameworks such as VectorBT, Backtrader, or QuantConnect LEAN.
Familiarity with PostgreSQL, TimescaleDB, cloud environments, Git, Docker, and CI/CD workflows.
Combines strong software engineering with deep quantitative finance and algorithmic trading expertise.
Experienced in building scalable, production-grade quantitative research infrastructure and improving portfolio Sharpe ratio.
Has working knowledge of ML techniques (XGBoost, LightGBM, PyTorch) and experience collaborating with execution/platform engineering teams in institutional trading environments.