Back testing Quant Researcher/Developer - REMOTE

A leading proprietary algorithmic trading firm is seeking a Backtesting Quant Researcher / Developer Lead to join its systematic mid‑frequency trading team.

The firm trades systematically across major global exchanges and a broad range of asset classes, including equities, equity derivatives, options, commodity futures, and rates.

Its strategy stack is focused on mid‑frequency trading approaches supported by robust research and technology.

The organization is highly technology‑driven, with 200+ professionals distributed globally across the United States, UAE, and Hong Kong.

While maintaining physical office locations, the firm operates as a fully remote business, offering a flexible and collaborative working environment.

This role offers ownership of backtesting methodology, simulation realism, and statistical rigor, working closely with quantitative researchers and PMs to ensure research translates effectively into live, production trading.

It is well suited to someone who enjoys building robust backtesting frameworks, deeply analysing transaction costs and market impact, and raising the standard of systematic research.

-Own backtesting methodology, statistical rigor, and simulation realism.

-Design and build components of the firm's backtesting framework.

-Quantify, validate, and communicate backtest reliability to researchers and PMs.

-Define data quality, point‑in‑time correctness, and historical simulation standards.

-Improve backtesting efficiency through code and infrastructure optimisation.

-Stay close to academic and industry developments in strategy evaluation.

Nice to Have

-Experience incorporating regime‑dependent simulations, including volatility and liquidity regimes, and change‑point detection techniques

-Familiarity with machine‑learning‑driven strategy evaluation and research workflows

-Publications or recognised contributions within quantitative finance or related research communities

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Own backtesting methodology, statistical rigor, and simulation realism.

Design and build components of the firm's backtesting framework.

Quantify, validate, and communicate backtest reliability to researchers and PMs.

Define data quality, point‑in‑time correctness, and historical simulation standards.

Improve backtesting efficiency through code and infrastructure optimisation.

Stay close to academic and industry developments in strategy evaluation.

Nice to Have

Experience incorporating regime‑dependent simulations, including volatility and liquidity regimes, and change‑point detection techniques

Familiarity with machine‑learning‑driven strategy evaluation and research workflows

Publications or recognised contributions within quantitative finance or related research communities