Head of Algo Trading (AI)

About the role

Deeter Analytics is a privately held investment research and trading firm managing ~$500M of its own capital across public markets. We are hiring a Head of Algo to lead a dedicated algorithmic effort to turn our edge into systematic, AI-driven alpha. This is a hands-on leadership role at the intersection of research and live trading.

Responsibilities

• Strategy, end to end: hypothesis generation, signal research, statistical validation, backtesting under realistic assumptions, live deployment, P&L attribution, and ongoing iteration.
• The research-to-production pipeline: signal generation, portfolio construction and sizing, and execution logic: the full path from an idea to a live position.
• Two complementary approaches: developing proprietary models and leveraging existing frontier AI models, with the judgment to choose the right one for each problem.
• Team and collaboration: building a small, high-caliber team as the effort scales, and working closely with the discretionary, engineering, and data teams.

Qualifications

• A strong track record of building and shipping AI trading systems end to end.
• Strong quantitative and technical depth.
• Fluent in modern AI, able to use it to accelerate research, generate and test ideas, and support decisions across the team.
• Empirical and iterative: establish baselines, run efficient experiments, and compound learnings over time, while holding live-capital work to a high standard.
• Pragmatic: select the right method for each problem and prioritize what moves P&L, rather than complexity for its own sake.
• Clear and collaborative: communicate concisely, give and receive feedback well, and work effectively across teams.
• Core skills: modeling, classical and frontier; deep facility with statistical and machine-learning modeling, experiment design & data sense, signal-versus-noise judgment, operational build-out, prototype to production.

What you’ll own

• Strategy, end to end: hypothesis generation, signal research, statistical validation, backtesting under realistic assumptions, live deployment, P&L attribution, and ongoing iteration.
• The research-to-production pipeline: signal generation, portfolio construction and sizing, and execution logic: the full path from an idea to a live position.
• Two complementary approaches: developing proprietary models and leveraging existing frontier AI models, with the judgment to choose the right one for each problem.
• Team and collaboration: building a small, high-caliber team as the effort scales, and working closely with the discretionary, engineering, and data teams.