Algorithmic Trader — Mid-Level

What you will do

• Research, build and run systematic strategies end to end — hypothesis, data, signal, backtest, sized pilot, production deployment, monitoring and retirement.
• Operate both sides of the book: delta-neutral strategies (spot/perp basis, funding capture, cross-venue and cross-instrument relative value, calendar spreads, market-neutral carry) and directional strategies (trend, momentum, mean-reversion, volatility-regime and systematic timing).
• Own the exposure profile of your strategies intraday — hedge ratios, leg risk, margin utilisation, funding and financing cost, and the residual delta you are actually carrying.
• Trade across major centralised venues and, where the opportunity sits there, on-chain perp and AMM venues.
• Build and maintain your own research stack: data ingestion and normalisation, feature pipelines, backtest harness, and P&L attribution you can defend line by line.
• Instrument every live strategy — fill quality, realised versus modelled slippage, adverse selection, capacity, alpha decay — and act on what the instrumentation tells you.
• Work directly with engineering on execution quality, connectivity resilience, and order-management correctness.
• Operate inside the fund risk framework: position and gross limits, drawdown thresholds, correlation budgets, pre-agreed kill criteria — escalating early rather than late.
• Document what you test, including what failed. The research library is a shared asset.

What you need

• You have personally built and run trading strategies with real capital at risk — not only researched or supported them — and can walk an interviewer through the full lifecycle of at least two of them.
• Direct, hands-on experience building both delta-neutral and directional strategies. You can explain the economic reason each one makes money, the regime it fails in, and how you sized it.
• A track record you can prove and demonstrate at interview: live P&L curves, live-versus-backtest divergence, drawdown history, turnover, capacity estimates and per-strategy attribution — ready to be interrogated in detail.
• Python fluency sufficient to own research independently end to end — data wrangling at scale, vectorised and event-driven backtesting, and code clean enough to hand to production.
• Genuine backtesting rigour. You can name and control for look-ahead, survivorship, fee/funding/slippage modelling, multiple-hypothesis testing, regime dependence and out-of-sample discipline.
• Working command of crypto market microstructure: order-book dynamics, maker/taker fee tiers and rebates, perpetual funding mechanics, basis behaviour, liquidation cascades, cross- and portfolio-margin.
• Practical exchange connectivity experience — REST/WebSocket/FIX, rate limits and throttling, order types, position and balance reconciliation, and the specific ways venues fail.
• Demonstrated risk instinct: correct sizing, awareness of correlation between supposedly independent legs, tail behaviour, and a considered answer to what you do when a venue halts withdrawals or a hedge leg stops filling.

Bonus

• A systems language — Rust, C++ or Go — for latency-sensitive execution components.
• On-chain execution experience: DEX perpetuals, AMM liquidity provision, MEV awareness, bridging and settlement risk.
• Derivatives and volatility fluency — Greeks, hedging listed or OTC vol, surface construction.
• Time on a market-making or liquidity-provision desk, and an understanding of how a market maker inventory and a fund alpha book differ.
• Postgraduate work in a quantitative discipline, or an equivalent body of demonstrated technical rigour.

How we hire

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