Quantitative Research Analyst (Systematic Trading Strategies)

Required Qualifications

• Effectiveness
• 1. A material decision having been taken on the strength of your analysis, and knowledge of the outcome.
• 2. The capacity to work effectively as the sole analyst, without a team across which workload or credit is distributed, and evidence of having previously reached an unusually high standard in something demanding, whether professional or otherwise.
• 3. A disposition to treat an assertion that something cannot be done as a proposition to be tested, and to exercise your own judgement where no guidance is available.
• 4. The ability to become effective in an unfamiliar domain quickly.
• 5. Intellectual honesty: a willingness to report findings that contradict a position you have previously advanced.
• 6. Daily, substantive use of AI coding and research tools such as Claude Code, Codex or Copilot. Our minimum expectation is that such tools are embedded in your core work, that you have established repeatable systems rather than isolated prompts, and that you can articulate their effect on the quality or speed of your output.
• Problem Solving
• 1. Exceptional analytical ability: the capacity to structure an ambiguous problem, decompose it and identify which component determines the answer.
• 2. Rigorous empirical judgement, including appropriate scepticism towards results that appear unusually favourable, and the discipline to establish the cause before drawing a conclusion.
• Communication
• 1. Excellent written and spoken English, and the ability to convey a technical result to a non-specialist audience without loss of substance.
• 2. The ability to sustain a position under challenge by reference to evidence, and to revise it where the evidence warrants.
• PREFERRED
• 1. Three or more years in quantitative research, systematic trading or data-intensive analytics, or a demonstrable record of independently developing and testing systematic strategies.
• 2. Familiarity with the specific mechanisms by which a backtest misleads, including look-ahead bias, survivorship bias, in-sample parameter selection, and unmodelled transaction and borrowing costs.
• 3. Working knowledge of equity market mechanics — order types, liquidity, trading halts and transaction costs — and experience of US equity markets in particular.
• 4. The ability to read and modify object-oriented code, for example C#.
• 5. A degree in mathematics, statistics, physics, computer science, engineering or quantitative finance.